Local memory sharing between kernels

ABSTRACT

One embodiment provides for a general-purpose graphics processing unit comprising a set of processing elements to execute one or more thread groups of a second kernel to be executed by the general-purpose graphics processor, an on-chip memory coupled to the set of processing elements, and a scheduler coupled with the set of processing elements, the scheduler to schedule the thread groups of the kernel to the set of processing elements, wherein the scheduler is to schedule a thread group of the second kernel to execute subsequent to a thread group of a first kernel, the thread group of the second kernel configured to access a region of the on-chip memory that contains data written by the thread group of the first kernel in response to a determination that the second kernel is dependent upon the first kernel.

CROSS REFERENCE TO RELATED APPLICATIONS

The present patent application is a continuation application claimingpriority from U.S. application Ser. No. 16/354,957, filed Mar. 15, 2019,the contents of which are incorporated herein in their entirety byreference.

FIELD

Embodiments relate generally to data processing and more particularly todata processing via a general-purpose graphics processing unit.

BACKGROUND OF THE DESCRIPTION

Current parallel graphics data processing includes systems and methodsdeveloped to perform specific operations on graphics data such as, forexample, linear interpolation, tessellation, rasterization, texturemapping, depth testing, etc. Traditionally, graphics processors usedfixed function computational units to process graphics data; however,more recently, portions of graphics processors have been madeprogrammable, enabling such processors to support a wider variety ofoperations for processing vertex and fragment data.

To further increase performance, graphics processors typically implementprocessing techniques such as pipelining that attempt to process, inparallel, as much graphics data as possible throughout the differentparts of the graphics pipeline. Parallel graphics processors with singleinstruction, multiple thread (SIMT) architectures are designed tomaximize the amount of parallel processing in the graphics pipeline. Inan SIMT architecture, groups of parallel threads attempt to executeprogram instructions synchronously together as often as possible toincrease processing efficiency. A general overview of software andhardware for SIMT architectures can be found in Shane Cook, CUDAProgramming Chapter 3, pages 37-51 (2013).

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements, and in which:

FIG. 1 is a block diagram illustrating a computer system configured toimplement one or more aspects of the embodiments described herein;

FIG. 2A-2D illustrate parallel processor components, according to anembodiment;

FIG. 3A-3C are block diagrams of graphics multiprocessors andmultiprocessor-based GPUs, according to embodiments;

FIG. 4A-4F illustrate an exemplary architecture in which a plurality ofGPUs is communicatively coupled to a plurality of multi-core processors;

FIG. 5 illustrates a graphics processing pipeline, according to anembodiment;

FIG. 6 illustrates a machine learning software stack, according to anembodiment;

FIG. 7 illustrates a general-purpose graphics processing unit, accordingto an embodiment;

FIG. 8 illustrates a multi-GPU computing system, according to anembodiment;

FIG. 9A-9B illustrate layers of exemplary deep neural networks;

FIG. 10 illustrates an exemplary recurrent neural network;

FIG. 11 illustrates training and deployment of a deep neural network;

FIG. 12 is a block diagram illustrating distributed learning;

FIG. 13 illustrates an exemplary inferencing system on a chip (SOC)suitable for performing inferencing using a trained model;

FIG. 14 is a block diagram of a data processing system, according to anembodiment;

FIG. 15 illustrates an engine block partition of a GPGPU, according toan embodiment;

FIG. 16A-16B illustrate shared local memory management, according toembodiments;

FIG. 17A-17B illustrate hardware and software operations oninterdependent kernels, according to embodiments described herein;

FIG. 18 illustrates concurrent execution of interdependent threadgroups, according to embodiments;

FIG. 19 illustrates a method of local memory sharing between kernels,according to an embodiment;

FIG. 20 illustrates an additional method of local memory sharing betweenkernels;

FIG. 21 is a block diagram of a processing system, according to anembodiment;

FIG. 22 is a block diagram of a processor according to an embodiment;

FIG. 23 is a block diagram of a graphics processor, according to anembodiment;

FIG. 24 is a block diagram of a graphics processing engine of a graphicsprocessor in accordance with some embodiments;

FIG. 25 is a block diagram of hardware logic of a graphics processorcore, according to some embodiments described herein;

FIG. 26A-26B illustrate thread execution logic including an array ofprocessing elements employed in a graphics processor core according toembodiments described herein;

FIG. 27 is a block diagram illustrating a graphics processor instructionformats according to some embodiments;

FIG. 28 is a block diagram of a graphics processor according to anotherembodiment;

FIG. 29A-29B illustrate a graphics processor command format and commandsequence, according to some embodiments;

FIG. 30 illustrates exemplary graphics software architecture for a dataprocessing system according to some embodiments;

FIG. 31A is a block diagram illustrating an IP core development system,according to an embodiment;

FIG. 31B illustrates a cross-section side view of an integrated circuitpackage assembly, according to some embodiments described herein;

FIG. 32 is a block diagram illustrating an exemplary system on a chipintegrated circuit, according to an embodiment; and

FIG. 33A-33B are block diagrams illustrating exemplary graphicsprocessors for use within an SoC, according to embodiments describedherein.

DETAILED DESCRIPTION

In some embodiments, a graphics processing unit (GPU) is communicativelycoupled to host/processor cores to accelerate graphics operations,machine-learning operations, pattern analysis operations, and variousgeneral-purpose GPU (GPGPU) functions. The GPU may be communicativelycoupled to the host processor/cores over a bus or another interconnect(e.g., a high-speed interconnect such as PCIe or NVLink). In otherembodiments, the GPU may be integrated on the same package or chip asthe cores and communicatively coupled to the cores over an internalprocessor bus/interconnect (i.e., internal to the package or chip).Regardless of the manner in which the GPU is connected, the processorcores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

In the following description, numerous specific details are set forth toprovide a more thorough understanding. However, it will be apparent toone of skill in the art that the embodiments described herein may bepracticed without one or more of these specific details. In otherinstances, well-known features have not been described to avoidobscuring the details of the present embodiments.

System Overview

FIG. 1 is a block diagram illustrating a computing system 100 configuredto implement one or more aspects of the embodiments described herein.The computing system 100 includes a processing subsystem 101 having oneor more processor(s) 102 and a system memory 104 communicating via aninterconnection path that may include a memory hub 105. The memory hub105 may be a separate component within a chipset component or may beintegrated within the one or more processor(s) 102. The memory hub 105couples with an I/O subsystem 111 via a communication link 106. The I/Osubsystem 111 includes an I/O hub 107 that can enable the computingsystem 100 to receive input from one or more input device(s) 108.Additionally, the I/O hub 107 can enable a display controller, which maybe included in the one or more processor(s) 102, to provide outputs toone or more display device(s) 110A. In one embodiment the one or moredisplay device(s) 110A coupled with the I/O hub 107 can include a local,internal, or embedded display device.

In one embodiment the processing subsystem 101 includes one or moreparallel processor(s) 112 coupled to memory hub 105 via a bus or othercommunication link 113. The communication link 113 may be one of anynumber of standards-based communication link technologies or protocols,such as, but not limited to PCI Express, or may be a vendor specificcommunications interface or communications fabric. In one embodiment theone or more parallel processor(s) 112 form a computationally focusedparallel or vector processing system that can include a large number ofprocessing cores and/or processing clusters, such as a many integratedcore (MIC) processor. In one embodiment the one or more parallelprocessor(s) 112 form a graphics processing subsystem that can outputpixels to one of the one or more display device(s) 110A coupled via theI/O hub 107. The one or more parallel processor(s) 112 can also includea display controller and display interface (not shown) to enable adirect connection to one or more display device(s) 110B.

Within the I/O subsystem 111, a system storage unit 114 can connect tothe I/O hub 107 to provide a storage mechanism for the computing system100. An I/O switch 116 can be used to provide an interface mechanism toenable connections between the I/O hub 107 and other components, such asa network adapter 118 and/or wireless network adapter 119 that may beintegrated into the platform, and various other devices that can beadded via one or more add-in device(s) 120. The network adapter 118 canbe an Ethernet adapter or another wired network adapter. The wirelessnetwork adapter 119 can include one or more of a Wi-Fi, Bluetooth, nearfield communication (NFC), or other network device that includes one ormore wireless radios.

The computing system 100 can include other components not explicitlyshown, including USB or other port connections, optical storage drives,video capture devices, and the like, may also be connected to the I/Ohub 107. Communication paths interconnecting the various components inFIG. 1 may be implemented using any suitable protocols, such as PCI(Peripheral Component Interconnect) based protocols (e.g., PCI-Express),or any other bus or point-to-point communication interfaces and/orprotocol(s), such as the NV-Link high-speed interconnect, orinterconnect protocols known in the art.

In one embodiment, the one or more parallel processor(s) 112 incorporatecircuitry optimized for graphics and video processing, including, forexample, video output circuitry, and constitutes a graphics processingunit (GPU). In another embodiment, the one or more parallel processor(s)112 incorporate circuitry optimized for general purpose processing,while preserving the underlying computational architecture, described ingreater detail herein. In yet another embodiment, components of thecomputing system 100 may be integrated with one or more other systemelements on a single integrated circuit. For example, the one or moreparallel processor(s) 112, memory hub 105, processor(s) 102, and I/O hub107 can be integrated into a system on chip (SoC) integrated circuit.Alternatively, the components of the computing system 100 can beintegrated into a single package to form a system in package (SIP)configuration. In one embodiment at least a portion of the components ofthe computing system 100 can be integrated into a multi-chip module(MCM), which can be interconnected with other multi-chip modules into amodular computing system.

It will be appreciated that the computing system 100 shown herein isillustrative and that variations and modifications are possible. Theconnection topology, including the number and arrangement of bridges,the number of processor(s) 102, and the number of parallel processor(s)112, may be modified as desired. For instance, in some embodiments,system memory 104 is connected to the processor(s) 102 directly ratherthan through a bridge, while other devices communicate with systemmemory 104 via the memory hub 105 and the processor(s) 102. In otheralternative topologies, the parallel processor(s) 112 are connected tothe I/O hub 107 or directly to one of the one or more processor(s) 102,rather than to the memory hub 105. In other embodiments, the I/O hub 107and memory hub 105 may be integrated into a single chip. Someembodiments may include two or more sets of processor(s) 102 attachedvia multiple sockets, which can couple with two or more instances of theparallel processor(s) 112.

Some of the particular components shown herein are optional and may notbe included in all implementations of the computing system 100. Forexample, any number of add-in cards or peripherals may be supported, orsome components may be eliminated. Furthermore, some architectures mayuse different terminology for components similar to those illustrated inFIG. 1. For example, the memory hub 105 may be referred to as aNorthbridge in some architectures, while the I/O hub 107 may be referredto as a Southbridge.

FIG. 2A illustrates a parallel processor 200, according to anembodiment. The various components of the parallel processor 200 may beimplemented using one or more integrated circuit devices, such asprogrammable processors, application specific integrated circuits(ASICs), or field programmable gate arrays (FPGA). The illustratedparallel processor 200 is a variant of the one or more parallelprocessor(s) 112 shown in FIG. 1, according to an embodiment.

In one embodiment the parallel processor 200 includes a parallelprocessing unit 202. The parallel processing unit includes an I/O unit204 that enables communication with other devices, including otherinstances of the parallel processing unit 202. The I/O unit 204 may bedirectly connected to other devices. In one embodiment the I/O unit 204connects with other devices via the use of a hub or switch interface,such as memory hub 105. The connections between the memory hub 105 andthe I/O unit 204 form a communication link 113. Within the parallelprocessing unit 202, the I/O unit 204 connects with a host interface 206and a memory crossbar 216, where the host interface 206 receivescommands directed to performing processing operations and the memorycrossbar 216 receives commands directed to performing memory operations.

When the host interface 206 receives a command buffer via the I/O unit204, the host interface 206 can direct work operations to perform thosecommands to a front end 208. In one embodiment the front end 208 coupleswith a scheduler 210, which is configured to distribute commands orother work items to a processing cluster array 212. In one embodimentthe scheduler 210 ensures that the processing cluster array 212 isproperly configured and in a valid state before tasks are distributed tothe processing clusters of the processing cluster array 212. In oneembodiment the scheduler 210 is implemented via firmware logic executingon a microcontroller. The microcontroller implemented scheduler 210 isconfigurable to perform complex scheduling and work distributionoperations at coarse and fine granularity, enabling rapid preemption andcontext switching of threads executing on the processing array 212. Inone embodiment, the host software can prove workloads for scheduling onthe processing array 212 via one of multiple graphics processingdoorbells. The workloads can then be automatically distributed acrossthe processing array 212 by the scheduler 210 logic within the schedulermicrocontroller.

The processing cluster array 212 can include up to “N” processingclusters (e.g., cluster 214A, cluster 214B, through cluster 214N). Eachcluster 214A-214N of the processing cluster array 212 can execute alarge number of concurrent threads. The scheduler 210 can allocate workto the clusters 214A-214N of the processing cluster array 212 usingvarious scheduling and/or work distribution algorithms, which may varydepending on the workload arising for each type of program orcomputation. The scheduling can be handled dynamically by the scheduler210, or can be assisted in part by compiler logic during compilation ofprogram logic configured for execution by the processing cluster array212. In one embodiment, different clusters 214A-214N of the processingcluster array 212 can be allocated for processing different types ofprograms or for performing different types of computations.

The processing cluster array 212 can be configured to perform varioustypes of parallel processing operations. In one embodiment theprocessing cluster array 212 is configured to perform general-purposeparallel compute operations. For example, the processing cluster array212 can include logic to execute processing tasks including filtering ofvideo and/or audio data, performing modeling operations, includingphysics operations, and performing data transformations.

In one embodiment the processing cluster array 212 is configured toperform parallel graphics processing operations. In embodiments in whichthe parallel processor 200 is configured to perform graphics processingoperations, the processing cluster array 212 can include additionallogic to support the execution of such graphics processing operations,including, but not limited to texture sampling logic to perform textureoperations, as well as tessellation logic and other vertex processinglogic. Additionally, the processing cluster array 212 can be configuredto execute graphics processing related shader programs such as, but notlimited to vertex shaders, tessellation shaders, geometry shaders, andpixel shaders. The parallel processing unit 202 can transfer data fromsystem memory via the I/O unit 204 for processing. During processing thetransferred data can be stored to on-chip memory (e.g., parallelprocessor memory 222) during processing, then written back to systemmemory.

In one embodiment, when the parallel processing unit 202 is used toperform graphics processing, the scheduler 210 can be configured todivide the processing workload into approximately equal sized tasks, tobetter enable distribution of the graphics processing operations tomultiple clusters 214A-214N of the processing cluster array 212. In someembodiments, portions of the processing cluster array 212 can beconfigured to perform different types of processing. For example a firstportion may be configured to perform vertex shading and topologygeneration, a second portion may be configured to perform tessellationand geometry shading, and a third portion may be configured to performpixel shading or other screen space operations, to produce a renderedimage for display. Intermediate data produced by one or more of theclusters 214A-214N may be stored in buffers to allow the intermediatedata to be transmitted between clusters 214A-214N for furtherprocessing.

During operation, the processing cluster array 212 can receiveprocessing tasks to be executed via the scheduler 210, which receivescommands defining processing tasks from front end 208. For graphicsprocessing operations, processing tasks can include indices of data tobe processed, e.g., surface (patch) data, primitive data, vertex data,and/or pixel data, as well as state parameters and commands defining howthe data is to be processed (e.g., what program is to be executed). Thescheduler 210 may be configured to fetch the indices corresponding tothe tasks or may receive the indices from the front end 208. The frontend 208 can be configured to ensure the processing cluster array 212 isconfigured to a valid state before the workload specified by incomingcommand buffers (e.g., batch-buffers, push buffers, etc.) is initiated.

Each of the one or more instances of the parallel processing unit 202can couple with parallel processor memory 222. The parallel processormemory 222 can be accessed via the memory crossbar 216, which canreceive memory requests from the processing cluster array 212 as well asthe I/O unit 204. The memory crossbar 216 can access the parallelprocessor memory 222 via a memory interface 218. The memory interface218 can include multiple partition units (e.g., partition unit 220A,partition unit 220B, through partition unit 220N) that can each coupleto a portion (e.g., memory unit) of parallel processor memory 222. Inone implementation the number of partition units 220A-220N is configuredto be equal to the number of memory units, such that a first partitionunit 220A has a corresponding first memory unit 224A, a second partitionunit 220B has a corresponding memory unit 224B, and an Nth partitionunit 220N has a corresponding Nth memory unit 224N. In otherembodiments, the number of partition units 220A-220N may not be equal tothe number of memory devices.

In various embodiments, the memory units 224A-224N can include varioustypes of memory devices, including dynamic random access memory (DRAM)or graphics random access memory, such as synchronous graphics randomaccess memory (SGRAM), including graphics double data rate (GDDR)memory. In one embodiment, the memory units 224A-224N may also include3D stacked memory, including but not limited to high bandwidth memory(HBM). Persons skilled in the art will appreciate that the specificimplementation of the memory units 224A-224N can vary, and can beselected from one of various conventional designs. Render targets, suchas frame buffers or texture maps may be stored across the memory units224A-224N, allowing partition units 220A-220N to write portions of eachrender target in parallel to efficiently use the available bandwidth ofparallel processor memory 222. In some embodiments, a local instance ofthe parallel processor memory 222 may be excluded in favor of a unifiedmemory design that utilizes system memory in conjunction with localcache memory.

In one embodiment, any one of the clusters 214A-214N of the processingcluster array 212 can process data that will be written to any of thememory units 224A-224N within parallel processor memory 222. The memorycrossbar 216 can be configured to transfer the output of each cluster214A-214N to any partition unit 220A-220N or to another cluster214A-214N, which can perform additional processing operations on theoutput. Each cluster 214A-214N can communicate with the memory interface218 through the memory crossbar 216 to read from or write to variousexternal memory devices. In one embodiment the memory crossbar 216 has aconnection to the memory interface 218 to communicate with the I/O unit204, as well as a connection to a local instance of the parallelprocessor memory 222, enabling the processing units within the differentprocessing clusters 214A-214N to communicate with system memory or othermemory that is not local to the parallel processing unit 202. In oneembodiment the memory crossbar 216 can use virtual channels to separatetraffic streams between the clusters 214A-214N and the partition units220A-220N.

While a single instance of the parallel processing unit 202 isillustrated within the parallel processor 200, any number of instancesof the parallel processing unit 202 can be included. For example,multiple instances of the parallel processing unit 202 can be providedon a single add-in card, or multiple add-in cards can be interconnected.The different instances of the parallel processing unit 202 can beconfigured to inter-operate even if the different instances havedifferent numbers of processing cores, different amounts of localparallel processor memory, and/or other configuration differences. Forexample, in one embodiment some instances of the parallel processingunit 202 can include higher precision floating point units relative toother instances. Systems incorporating one or more instances of theparallel processing unit 202 or the parallel processor 200 can beimplemented in a variety of configurations and form factors, includingbut not limited to desktop, laptop, or handheld personal computers,servers, workstations, game consoles, and/or embedded systems.

FIG. 2B is a block diagram of a partition unit 220, according to anembodiment. In one embodiment the partition unit 220 is an instance ofone of the partition units 220A-220N of FIG. 2A. As illustrated, thepartition unit 220 includes an L2 cache 221, a frame buffer interface225, and a ROP 226 (raster operations unit). The L2 cache 221 is aread/write cache that is configured to perform load and store operationsreceived from the memory crossbar 216 and ROP 226. Read misses andurgent write-back requests are output by L2 cache 221 to frame bufferinterface 225 for processing. Updates can also be sent to the framebuffer via the frame buffer interface 225 for processing. In oneembodiment the frame buffer interface 225 interfaces with one of thememory units in parallel processor memory, such as the memory units224A-224N of FIG. 2A (e.g., within parallel processor memory 222).

In graphics applications, the ROP 226 is a processing unit that performsraster operations such as stencil, z test, blending, and the like. TheROP 226 then outputs processed graphics data that is stored in graphicsmemory. In some embodiments the ROP 226 includes compression logic tocompress depth or color data that is written to memory and decompressdepth or color data that is read from memory. The compression logic canbe lossless compression logic that makes use of one or more of multiplecompression algorithms. The type of compression that is performed by theROP 226 can vary based on the statistical characteristics of the data tobe compressed. For example, in one embodiment, delta color compressionis performed on depth and color data on a per-tile basis.

In some embodiments, the ROP 226 is included within each processingcluster (e.g., cluster 214A-214N of FIG. 2A) instead of within thepartition unit 220. In such embodiment, read and write requests forpixel data are transmitted over the memory crossbar 216 instead of pixelfragment data. The processed graphics data may be displayed on a displaydevice, such as one of the one or more display device(s) 110 of FIG. 1,routed for further processing by the processor(s) 102, or routed forfurther processing by one of the processing entities within the parallelprocessor 200 of FIG. 2A.

FIG. 2C is a block diagram of a processing cluster 214 within a parallelprocessing unit, according to an embodiment. In one embodiment theprocessing cluster is an instance of one of the processing clusters214A-214N of FIG. 2A. The processing cluster 214 can be configured toexecute many threads in parallel, where the term “thread” refers to aninstance of a particular program executing on a particular set of inputdata. In some embodiments, single-instruction, multiple-data (SIMD)instruction issue techniques are used to support parallel execution of alarge number of threads without providing multiple independentinstruction units. In other embodiments, single-instruction,multiple-thread (SIMT) techniques are used to support parallel executionof a large number of generally synchronized threads, using a commoninstruction unit configured to issue instructions to a set of processingengines within each one of the processing clusters. Unlike a SIMDexecution regime, where all processing engines typically executeidentical instructions, SIMT execution allows different threads to morereadily follow divergent execution paths through a given thread program.Persons skilled in the art will understand that a SIMD processing regimerepresents a functional subset of a SIMT processing regime.

Operation of the processing cluster 214 can be controlled via a pipelinemanager 232 that distributes processing tasks to SIMT parallelprocessors. The pipeline manager 232 receives instructions from thescheduler 210 of FIG. 2A and manages execution of those instructions viaa graphics multiprocessor 234 and/or a texture unit 236. The illustratedgraphics multiprocessor 234 is an exemplary instance of a SIMT parallelprocessor. However, various types of SIMT parallel processors ofdiffering architectures may be included within the processing cluster214. One or more instances of the graphics multiprocessor 234 can beincluded within a processing cluster 214. The graphics multiprocessor234 can process data and a data crossbar 240 can be used to distributethe processed data to one of multiple possible destinations, includingother shader units. The pipeline manager 232 can facilitate thedistribution of processed data by specifying destinations for processeddata to be distributed via the data crossbar 240.

Each graphics multiprocessor 234 within the processing cluster 214 caninclude an identical set of functional execution logic (e.g., arithmeticlogic units, load-store units, etc.). The functional execution logic canbe configured in a pipelined manner in which new instructions can beissued before previous instructions are complete. The functionalexecution logic supports a variety of operations including integer andfloating point arithmetic, comparison operations, Boolean operations,bit-shifting, and computation of various algebraic functions. In oneembodiment the same functional-unit hardware can be leveraged to performdifferent operations and any combination of functional units may bepresent.

The instructions transmitted to the processing cluster 214 constitutes athread. A set of threads executing across the set of parallel processingengines is a thread group. A thread group executes the same program ondifferent input data. Each thread within a thread group can be assignedto a different processing engine within a graphics multiprocessor 234. Athread group may include fewer threads than the number of processingengines within the graphics multiprocessor 234. When a thread groupincludes fewer threads than the number of processing engines, one ormore of the processing engines may be idle during cycles in which thatthread group is being processed. A thread group may also include morethreads than the number of processing engines within the graphicsmultiprocessor 234. When the thread group includes more threads than thenumber of processing engines within the graphics multiprocessor 234,processing can be performed over consecutive clock cycles. In oneembodiment multiple thread groups can be executed concurrently on agraphics multiprocessor 234.

In one embodiment the graphics multiprocessor 234 includes an internalcache memory to perform load and store operations. In one embodiment,the graphics multiprocessor 234 can forego an internal cache and use acache memory (e.g., L1 cache 248) within the processing cluster 214.Each graphics multiprocessor 234 also has access to L2 caches within thepartition units (e.g., partition units 220A-220N of FIG. 2A) that areshared among all processing clusters 214 and may be used to transferdata between threads. The graphics multiprocessor 234 may also accessoff-chip global memory, which can include one or more of local parallelprocessor memory and/or system memory. Any memory external to theparallel processing unit 202 may be used as global memory. Embodimentsin which the processing cluster 214 includes multiple instances of thegraphics multiprocessor 234 can share common instructions and data,which may be stored in the L1 cache 248.

Each processing cluster 214 may include an MMU 245 (memory managementunit) that is configured to map virtual addresses into physicaladdresses. In other embodiments, one or more instances of the MMU 245may reside within the memory interface 218 of FIG. 2A. The MMU 245includes a set of page table entries (PTEs) used to map a virtualaddress to a physical address of a tile and optionally a cache lineindex. The MMU 245 may include address translation lookaside buffers(TLB) or caches that may reside within the graphics multiprocessor 234or the L1 cache or processing cluster 214. The physical address isprocessed to distribute surface data access locality to allow efficientrequest interleaving among partition units. The cache line index may beused to determine whether a request for a cache line is a hit or miss.

In graphics and computing applications, a processing cluster 214 may beconfigured such that each graphics multiprocessor 234 is coupled to atexture unit 236 for performing texture mapping operations, e.g.,determining texture sample positions, reading texture data, andfiltering the texture data. Texture data is read from an internaltexture L1 cache (not shown) or in some embodiments from the L1 cachewithin graphics multiprocessor 234 and is fetched from an L2 cache,local parallel processor memory, or system memory, as needed. Eachgraphics multiprocessor 234 outputs processed tasks to the data crossbar240 to provide the processed task to another processing cluster 214 forfurther processing or to store the processed task in an L2 cache, localparallel processor memory, or system memory via the memory crossbar 216.A preROP 242 (pre-raster operations unit) is configured to receive datafrom graphics multiprocessor 234, direct data to ROP units, which may belocated with partition units as described herein (e.g., partition units220A-220N of FIG. 2A). The preROP 242 unit can perform optimizations forcolor blending, organize pixel color data, and perform addresstranslations.

It will be appreciated that the core architecture described herein isillustrative and that variations and modifications are possible. Anynumber of processing units, e.g., graphics multiprocessor 234, textureunits 236, preROPs 242, etc., may be included within a processingcluster 214. Further, while only one processing cluster 214 is shown, aparallel processing unit as described herein may include any number ofinstances of the processing cluster 214. In one embodiment, eachprocessing cluster 214 can be configured to operate independently ofother processing clusters 214 using separate and distinct processingunits, L1 caches, etc.

FIG. 2D shows a graphics multiprocessor 234, according to oneembodiment. In such embodiment the graphics multiprocessor 234 coupleswith the pipeline manager 232 of the processing cluster 214. Thegraphics multiprocessor 234 has an execution pipeline including but notlimited to an instruction cache 252, an instruction unit 254, an addressmapping unit 256, a register file 258, one or more general purposegraphics processing unit (GPGPU) cores 262, and one or more load/storeunits 266. The GPGPU cores 262 and load/store units 266 are coupled withcache memory 272 and shared memory 270 via a memory and cacheinterconnect 268. In one embodiment the graphics multiprocessor 234additionally includes tensor and/or ray-tracing cores 263 that includehardware logic to accelerate matrix and/or ray-tracing operations.

In one embodiment, the instruction cache 252 receives a stream ofinstructions to execute from the pipeline manager 232. The instructionsare cached in the instruction cache 252 and dispatched for execution bythe instruction unit 254. The instruction unit 254 can dispatchinstructions as thread groups (e.g., warps), with each thread of thethread group assigned to a different execution unit within GPGPU core262. An instruction can access any of a local, shared, or global addressspace by specifying an address within a unified address space. Theaddress mapping unit 256 can be used to translate addresses in theunified address space into a distinct memory address that can beaccessed by the load/store units 266.

The register file 258 provides a set of registers for the functionalunits of the graphics multiprocessor 234. The register file 258 providestemporary storage for operands connected to the data paths of thefunctional units (e.g., GPGPU cores 262, load/store units 266) of thegraphics multiprocessor 234. In one embodiment, the register file 258 isdivided between each of the functional units such that each functionalunit is allocated a dedicated portion of the register file 258. In oneembodiment, the register file 258 is divided between the different warpsbeing executed by the graphics multiprocessor 234.

The GPGPU cores 262 can each include floating point units (FPUs) and/orinteger arithmetic logic units (ALUs) that are used to executeinstructions of the graphics multiprocessor 234. The GPGPU cores 262 canbe similar in architecture or can differ in architecture, according toembodiments. For example and in one embodiment, a first portion of theGPGPU cores 262 include a single precision FPU and an integer ALU whilea second portion of the GPGPU cores include a double precision FPU. Inone embodiment the FPUs can implement the IEEE 754-2008 standard forfloating point arithmetic or enable variable precision floating pointarithmetic. The graphics multiprocessor 234 can additionally include oneor more fixed function or special function units to perform specificfunctions such as copy rectangle or pixel blending operations. In oneembodiment one or more of the GPGPU cores can also include fixed orspecial function logic.

In one embodiment the GPGPU cores 262 include SIMD logic capable ofperforming a single instruction on multiple sets of data. In oneembodiment GPGPU cores 262 can physically execute SIMD4, SIMD8, andSIMD16 instructions and logically execute SIMD1, SIMD2, and SIMD32instructions. The SIMD instructions for the GPGPU cores can be generatedat compile time by a shader compiler or automatically generated whenexecuting programs written and compiled for single program multiple data(SPMD) or SIMT architectures. Multiple threads of a program configuredfor the SIMT execution model can be executed via a single SIMDinstruction. For example and in one embodiment, eight SIMT threads thatperform the same or similar operations can be executed in parallel via asingle SIMD8 logic unit.

The memory and cache interconnect 268 is an interconnect network thatconnects each of the functional units of the graphics multiprocessor 234to the register file 258 and to the shared memory 270. In oneembodiment, the memory and cache interconnect 268 is a crossbarinterconnect that allows the load/store unit 266 to implement load andstore operations between the shared memory 270 and the register file258. The register file 258 can operate at the same frequency as theGPGPU cores 262, thus data transfer between the GPGPU cores 262 and theregister file 258 is very low latency. The shared memory 270 can be usedto enable communication between threads that execute on the functionalunits within the graphics multiprocessor 234. The cache memory 272 canbe used as a data cache for example, to cache texture data communicatedbetween the functional units and the texture unit 236. The shared memory270 can also be used as a program managed cached. Threads executing onthe GPGPU cores 262 can programmatically store data within the sharedmemory in addition to the automatically cached data that is storedwithin the cache memory 272.

FIG. 3A-3C illustrate additional graphics multiprocessors, according toembodiments. FIG. 3A-3B illustrate graphics multiprocessors 325, 350,which are variants of the graphics multiprocessor 234 of FIG. 2C. FIG.3C illustrates a graphics processing unit (GPU) 380 which includesdedicated sets of graphics processing resources arranged into multi-coregroups 365A-365N. The illustrated graphics multiprocessors 325, 350 andthe multi-core groups 365A-365N can be streaming multiprocessor (SM)capable of simultaneous execution of a large number of executionthreads.

FIG. 3A shows a graphics multiprocessor 325 according to an additionalembodiment. The graphics multiprocessor 325 includes multiple additionalinstances of execution resource units relative to the graphicsmultiprocessor 234 of FIG. 2D. For example, the graphics multiprocessor325 can include multiple instances of the instruction unit 332A-332B,register file 334A-334B, and texture unit(s) 344A-344B. The graphicsmultiprocessor 325 also includes multiple sets of graphics or computeexecution units (e.g., GPGPU core 336A-336B, tensor core 337A-337B,ray-tracing core 338A-338B) and multiple sets of load/store units340A-340B. In one embodiment the execution resource units have a commoninstruction cache 330, texture and/or data cache memory 342, and sharedmemory 346.

The various components can communicate via an interconnect fabric 327.In one embodiment the interconnect fabric 327 includes one or morecrossbar switches to enable communication between the various componentsof the graphics multiprocessor 325. In one embodiment the interconnectfabric 327 is a separate, high-speed network fabric layer upon whicheach component of the graphics multiprocessor 325 is stacked. Thecomponents of the graphics multiprocessor 325 communicate with remotecomponents via the interconnect fabric 327. For example, the GPGPU cores336A-336B, 337A-337B, and 3378A-338B can each communicate with sharedmemory 346 via the interconnect fabric 327. The interconnect fabric 327can arbitrate communication within the graphics multiprocessor 325 toensure a fair bandwidth allocation between components.

FIG. 3B shows a graphics multiprocessor 350 according to an additionalembodiment. The graphics processor includes multiple sets of executionresources 356A-356D, where each set of execution resource includesmultiple instruction units, register files, GPGPU cores, and load storeunits, as illustrated in FIG. 2D and FIG. 3A. The execution resources356A-356D can work in concert with texture unit(s) 360A-360D for textureoperations, while sharing an instruction cache 354, and shared memory353. In one embodiment the execution resources 356A-356D can share aninstruction cache 354 and shared memory 353, as well as multipleinstances of a texture and/or data cache memory 358A-358B. The variouscomponents can communicate via an interconnect fabric 352 similar to theinterconnect fabric 327 of FIG. 3A.

Persons skilled in the art will understand that the architecturedescribed in FIGS. 1, 2A-2D, and 3A-3B are descriptive and not limitingas to the scope of the present embodiments. Thus, the techniquesdescribed herein may be implemented on any properly configuredprocessing unit, including, without limitation, one or more mobileapplication processors, one or more desktop or server central processingunits (CPUs) including multi-core CPUs, one or more parallel processingunits, such as the parallel processing unit 202 of FIG. 2A, as well asone or more graphics processors or special purpose processing units,without departure from the scope of the embodiments described herein.

In some embodiments a parallel processor or GPGPU as described herein iscommunicatively coupled to host/processor cores to accelerate graphicsoperations, machine-learning operations, pattern analysis operations,and various general purpose GPU (GPGPU) functions. The GPU may becommunicatively coupled to the host processor/cores over a bus or otherinterconnect (e.g., a high speed interconnect such as PCIe or NVLink).In other embodiments, the GPU may be integrated on the same package orchip as the cores and communicatively coupled to the cores over aninternal processor bus/interconnect (i.e., internal to the package orchip). Regardless of the manner in which the GPU is connected, theprocessor cores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

FIG. 3C illustrates a graphics processing unit (GPU) 380 which includesdedicated sets of graphics processing resources arranged into multi-coregroups 365A-N. While the details of only a single multi-core group 365Aare provided, it will be appreciated that the other multi-core groups365B-365N may be equipped with the same or similar sets of graphicsprocessing resources.

As illustrated, a multi-core group 365A may include a set of graphicscores 370, a set of tensor cores 371, and a set of ray tracing cores372. A scheduler/dispatcher 368 schedules and dispatches the graphicsthreads for execution on the various cores 370, 371, 372. A set ofregister files 369 store operand values used by the cores 370, 371, 372when executing the graphics threads. These may include, for example,integer registers for storing integer values, floating point registersfor storing floating point values, vector registers for storing packeddata elements (integer and/or floating point data elements) and tileregisters for storing tensor/matrix values. In one embodiment, the tileregisters are implemented as combined sets of vector registers.

One or more combined level 1 (L1) caches and shared memory units 373store graphics data such as texture data, vertex data, pixel data, raydata, bounding volume data, etc., locally within each multi-core group365A. One or more texture units 374 can also be used to performtexturing operations, such as texture mapping and sampling. A Level 2(L2) cache 375 shared by all or a subset of the multi-core groups365A-365N stores graphics data and/or instructions for multipleconcurrent graphics threads. As illustrated, the L2 cache 375 may beshared across a plurality of multi-core groups 365A-365N. One or morememory controllers 367 couple the GPU 380 to a memory 366 which may be asystem memory (e.g., DRAM) and/or a dedicated graphics memory (e.g.,GDDR6 memory).

Input/output (I/O) circuitry 363 couples the GPU 380 to one or more I/Odevices 362 such as digital signal processors (DSPs), networkcontrollers, or user input devices. An on-chip interconnect may be usedto couple the I/O devices 362 to the GPU 380 and memory 366. One or moreI/O memory management units (IOMMUs) 364 of the I/O circuitry 3195couple the I/O devices 362 directly to the system memory 366. In oneembodiment, the IOMMU 364 manages multiple sets of page tables to mapvirtual addresses to physical addresses in system memory 366. In thisembodiment, the I/O devices 362, CPUs 361, and GPUs 380 may share thesame virtual address space.

In one implementation, the IOMMU 364 supports virtualization. In thiscase, it may manage a first set of page tables to map guest/graphicsvirtual addresses to guest/graphics physical addresses and a second setof page tables to map the guest/graphics physical addresses tosystem/host physical addresses (e.g., within system memory 366). Thebase addresses of each of the first and second sets of page tables maybe stored in control registers and swapped out on a context switch(e.g., so that the new context is provided with access to the relevantset of page tables). While not illustrated in FIG. 3C, each of the cores370, 371, 372 and/or multi-core groups 365A-365N may include translationlookaside buffers (TLBs) to cache guest virtual to guest physicaltranslations, guest physical to host physical translations, and guestvirtual to host physical translations.

In one embodiment, the CPUs 361, GPUs 380, and I/O devices 362 areintegrated on a single semiconductor chip and/or chip package. Theillustrated memory 366 may be integrated on the same chip or may becoupled to the memory controllers 367 via an off-chip interface. In oneimplementation, the memory 366 comprises GDDR6 memory which shares thesame virtual address space as other physical system-level memories,although the underlying principles of the invention are not limited tothis specific implementation.

In one embodiment, the tensor cores 371 include a plurality of executionunits specifically designed to perform matrix operations, which are thefundamental compute operation used to perform deep learning operations.For example, simultaneous matrix multiplication operations may be usedfor neural network training and inferencing. The tensor cores 371 mayperform matrix processing using a variety of operand precisionsincluding single precision floating-point (e.g., 32 bits),half-precision floating point (e.g., 16 bits), integer words (16 bits),bytes (8 bits), and half-bytes (4 bits). In one embodiment, a neuralnetwork implementation extracts features of each rendered scene,potentially combining details from multiple frames, to construct ahigh-quality final image.

In deep learning implementations, parallel matrix multiplication workmay be scheduled for execution on the tensor cores 371. The training ofneural networks, in particular, requires a significant number matrix dotproduct operations. In order to process an inner-product formulation ofan N×N×N matrix multiply, the tensor cores 371 may include at least Ndot-product processing elements. Before the matrix multiply begins, oneentire matrix is loaded into tile registers and at least one column of asecond matrix is loaded each cycle for N cycles. Each cycle, there are Ndot products that are processed.

Matrix elements may be stored at different precisions depending on theparticular implementation, including 16-bit words, 8-bit bytes (e.g.,INT8) and 4-bit half-bytes (e.g., INT4). Different precision modes maybe specified for the tensor cores 371 to ensure that the most efficientprecision is used for different workloads (e.g., such as inferencingworkloads which can tolerate quantization to bytes and half-bytes).

In one embodiment, the ray tracing cores 372 accelerate ray tracingoperations for both real-time ray tracing and non-real-time ray tracingimplementations. In particular, the ray tracing cores 372 include raytraversal/intersection circuitry for performing ray traversal usingbounding volume hierarchies (BVHs) and identifying intersections betweenrays and primitives enclosed within the BVH volumes. The ray tracingcores 372 may also include circuitry for performing depth testing andculling (e.g., using a Z buffer or similar arrangement). In oneimplementation, the ray tracing cores 372 perform traversal andintersection operations in concert with the image denoising techniquesdescribed herein, at least a portion of which may be executed on thetensor cores 371. For example, in one embodiment, the tensor cores 371implement a deep learning neural network to perform denoising of framesgenerated by the ray tracing cores 372. However, the CPUs 361, graphicscores 370, and/or ray tracing cores 372 may also implement all or aportion of the denoising and/or deep learning algorithms.

In addition, as described above, a distributed approach to denoising maybe employed in which the GPU 380 is in a computing device coupled toother computing devices over a network or high-speed interconnect. Inthis embodiment, the interconnected computing devices share neuralnetwork learning/training data to improve the speed with which theoverall system learns to perform denoising for different types of imageframes and/or different graphics applications.

In one embodiment, the ray tracing cores 372 process all BVH traversaland ray-primitive intersections, saving the graphics cores 370 frombeing overloaded with thousands of instructions per ray. In oneembodiment, each ray tracing core 372 includes a first set ofspecialized circuitry for performing bounding box tests (e.g., fortraversal operations) and a second set of specialized circuitry forperforming the ray-triangle intersection tests (e.g., intersecting rayswhich have been traversed). Thus, in one embodiment, the multi-coregroup 365A can simply launch a ray probe, and the ray tracing cores 372independently perform ray traversal and intersection and return hit data(e.g., a hit, no hit, multiple hits, etc.) to the thread context. Theother cores 370, 371 are freed to perform other graphics or compute workwhile the ray tracing cores 372 perform the traversal and intersectionoperations.

In one embodiment, each ray tracing core 372 includes a traversal unitto perform BVH testing operations and an intersection unit whichperforms ray-primitive intersection tests. The intersection unitgenerates a “hit”, “no hit”, or “multiple hit” response, which itprovides to the appropriate thread. During the traversal andintersection operations, the execution resources of the other cores(e.g., graphics cores 370 and tensor cores 371) are freed to performother forms of graphics work.

In one particular embodiment described below, a hybrid rasterization/raytracing approach is used in which work is distributed between thegraphics cores 370 and ray tracing cores 372.

In one embodiment, the ray tracing cores 372 (and/or other cores 370,371) include hardware support for a ray tracing instruction set such asMicrosoft's DirectX Ray Tracing (DXR) which includes a DispatchRayscommand, as well as ray-generation, closest-hit, any-hit, and missshaders, which enable the assignment of unique sets of shaders andtextures for each object. Another ray tracing platform which may besupported by the ray tracing cores 372, graphics cores 370 and tensorcores 371 is Vulkan 1.1.85. Note, however, that the underlyingprinciples of the invention are not limited to any particular raytracing ISA.

In general, the various cores 372, 371, 370 may support a ray tracinginstruction set that includes instructions/functions for ray generation,closest hit, any hit, ray-primitive intersection, per-primitive andhierarchical bounding box construction, miss, visit, and exceptions.More specifically, one embodiment includes ray tracing instructions toperform the following functions:

Ray Generation—Ray generation instructions may be executed for eachpixel, sample, or other user-defined work assignment.

Closest Hit—A closest hit instruction may be executed to locate theclosest intersection point of a ray with primitives within a scene.

Any Hit—An any hit instruction identifies multiple intersections betweena ray and primitives within a scene, potentially to identify a newclosest intersection point.

Intersection—An intersection instruction performs a ray-primitiveintersection test and outputs a result.

Per-primitive Bounding box Construction—This instruction builds abounding box around a given primitive or group of primitives (e.g., whenbuilding a new BVH or other acceleration data structure).

Miss—Indicates that a ray misses all geometry within a scene, orspecified region of a scene.

Visit—Indicates the children volumes a ray will traverse.

Exceptions—Includes various types of exception handlers (e.g., invokedfor various error conditions).

Techniques for GPU to Host Processor Interconnection

FIG. 4A illustrates an exemplary architecture in which a plurality ofGPUs 410-413 are communicatively coupled to a plurality of multi-coreprocessors 405-406 over high-speed links 440A-440D (e.g., buses,point-to-point interconnects, etc.). In one embodiment, the high-speedlinks 440A-440D support a communication throughput of 4 GB/s, 30 GB/s,80 GB/s or higher, depending on the implementation. Various interconnectprotocols may be used including, but not limited to, PCIe 4.0 or 5.0 andNVLink 2.0. However, the underlying principles of the invention are notlimited to any particular communication protocol or throughput.

In addition, in one embodiment, two or more of the GPUs 410-413 areinterconnected over high-speed links 442A-442B, which may be implementedusing the same or different protocols/links than those used forhigh-speed links 440A-440D. Similarly, two or more of the multi-coreprocessors 405-406 may be connected over high speed link 443 which maybe symmetric multi-processor (SMP) buses operating at 20 GB/s, 30 GB/s,120 GB/s or higher. Alternatively, all communication between the varioussystem components shown in FIG. 4A may be accomplished using the sameprotocols/links (e.g., over a common interconnection fabric). Asmentioned, however, the underlying principles of the invention are notlimited to any particular type of interconnect technology.

In one embodiment, each multi-core processor 405-406 is communicativelycoupled to a processor memory 401-402, via memory interconnects430A-430B, respectively, and each GPU 410-413 is communicatively coupledto GPU memory 420-423 over GPU memory interconnects 450A-450D,respectively. The memory interconnects 430A-430B and 450A-450D mayutilize the same or different memory access technologies. By way ofexample, and not limitation, the processor memories 401-402 and GPUmemories 420-423 may be volatile memories such as dynamic random accessmemories (DRAMs) (including stacked DRAMs), Graphics DDR SDRAM (GDDR)(e.g., GDDR5, GDDR6), or High Bandwidth Memory (HBM) and/or may benon-volatile memories such as 3D XPoint or Nano-Ram. In one embodiment,some portion of the memories may be volatile memory and another portionmay be non-volatile memory (e.g., using a two-level memory (2LM)hierarchy).

As described below, although the various processors 405-406 and GPUs410-413 may be physically coupled to a particular memory 401-402,420-423, respectively, a unified memory architecture may be implementedin which the same virtual system address space (also referred to as the“effective address” space) is distributed among all of the variousphysical memories. For example, processor memories 401-402 may eachcomprise 64 GB of the system memory address space and GPU memories420-423 may each comprise 32 GB of the system memory address space(resulting in a total of 256 GB addressable memory in this example).

FIG. 4B illustrates additional details for an interconnection between amulti-core processor 407 and a graphics acceleration module 446 inaccordance with one embodiment. The graphics acceleration module 446 mayinclude one or more GPU chips integrated on a line card which is coupledto the processor 407 via the high-speed link 440. Alternatively, thegraphics acceleration module 446 may be integrated on the same packageor chip as the processor 407.

The illustrated processor 407 includes a plurality of cores 460A-460D,each with a translation lookaside buffer 461A-461D and one or morecaches 462A-462D. The cores may include various other components forexecuting instructions and processing data which are not illustrated toavoid obscuring the underlying principles of the invention (e.g.,instruction fetch units, branch prediction units, decoders, executionunits, reorder buffers, etc.). The caches 462A-462D may comprise level 1(L1) and level 2 (L2) caches. In addition, one or more shared caches 456may be included in the caching hierarchy and shared by sets of the cores460A-460D. For example, one embodiment of the processor 407 includes 24cores, each with its own L1 cache, twelve shared L2 caches, and twelveshared L3 caches. In this embodiment, one of the L2 and L3 caches areshared by two adjacent cores. The processor 407 and the graphicsaccelerator integration module 446 connect with system memory 441, whichmay include processor memories 401-402.

Coherency is maintained for data and instructions stored in the variouscaches 462A-462D, 456 and system memory 441 via inter-core communicationover a coherence bus 464. For example, each cache may have cachecoherency logic/circuitry associated therewith to communicate to overthe coherence bus 464 in response to detected reads or writes toparticular cache lines. In one implementation, a cache snooping protocolis implemented over the coherence bus 464 to snoop cache accesses. Cachesnooping/coherency techniques are well understood by those of skill inthe art and will not be described in detail here to avoid obscuring theunderlying principles of the invention.

In one embodiment, a proxy circuit 425 communicatively couples thegraphics acceleration module 446 to the coherence bus 464, allowing thegraphics acceleration module 446 to participate in the cache coherenceprotocol as a peer of the cores. In particular, an interface 435provides connectivity to the proxy circuit 425 over high-speed link 440(e.g., a PCIe bus, NVLink, etc.) and an interface 437 connects thegraphics acceleration module 446 to the high-speed link 440.

In one implementation, an accelerator integration circuit 436 providescache management, memory access, context management, and interruptmanagement services on behalf of a plurality of graphics processingengines 431, 432, N of the graphics acceleration module 446. Thegraphics processing engines 431, 432, N may each comprise a separategraphics processing unit (GPU). Alternatively, the graphics processingengines 431, 432, N may comprise different types of graphics processingengines within a GPU such as graphics execution units, media processingengines (e.g., video encoders/decoders), samplers, and blit engines. Inother words, the graphics acceleration module may be a GPU with aplurality of graphics processing engines 431-432, N or the graphicsprocessing engines 431-432, N may be individual GPUs integrated on acommon package, line card, or chip.

In one embodiment, the accelerator integration circuit 436 includes amemory management unit (MMU) 439 for performing various memorymanagement functions such as virtual-to-physical memory translations(also referred to as effective-to-real memory translations) and memoryaccess protocols for accessing system memory 441. The MMU 439 may alsoinclude a translation lookaside buffer (TLB) (not shown) for caching thevirtual/effective to physical/real address translations. In oneimplementation, a cache 438 stores commands and data for efficientaccess by the graphics processing engines 431-432, N. In one embodiment,the data stored in cache 438 and graphics memories 433-434, M is keptcoherent with the core caches 462A-462D, 456 and system memory 411. Asmentioned, this may be accomplished via proxy circuit 425 which takespart in the cache coherency mechanism on behalf of cache 438 andmemories 433-434, M (e.g., sending updates to the cache 438 related tomodifications/accesses of cache lines on processor caches 462A-462D, 456and receiving updates from the cache 438).

A set of registers 445 store context data for threads executed by thegraphics processing engines 431-432, N and a context management circuit448 manages the thread contexts. For example, the context managementcircuit 448 may perform save and restore operations to save and restorecontexts of the various threads during contexts switches (e.g., where afirst thread is saved and a second thread is stored so that the secondthread can be execute by a graphics processing engine). For example, ona context switch, the context management circuit 448 may store currentregister values to a designated region in memory (e.g., identified by acontext pointer). It may then restore the register values when returningto the context. In one embodiment, an interrupt management circuit 447receives and processes interrupts received from system devices.

In one implementation, virtual/effective addresses from a graphicsprocessing engine 431 are translated to real/physical addresses insystem memory 411 by the MMU 439. One embodiment of the acceleratorintegration circuit 436 supports multiple (e.g., 4, 8, 16) graphicsaccelerator modules 446 and/or other accelerator devices. The graphicsaccelerator module 446 may be dedicated to a single application executedon the processor 407 or may be shared between multiple applications. Inone embodiment, a virtualized graphics execution environment ispresented in which the resources of the graphics processing engines431-432, N are shared with multiple applications or virtual machines(VMs). The resources may be subdivided into “slices” which are allocatedto different VMs and/or applications based on the processingrequirements and priorities associated with the VMs and/or applications.

Thus, the accelerator integration circuit acts as a bridge to the systemfor the graphics acceleration module 446 and provides addresstranslation and system memory cache services. In addition, theaccelerator integration circuit 436 may provide virtualizationfacilities for the host processor to manage virtualization of thegraphics processing engines, interrupts, and memory management.

Because hardware resources of the graphics processing engines 431-432, Nare mapped explicitly to the real address space seen by the hostprocessor 407, any host processor can address these resources directlyusing an effective address value. One function of the acceleratorintegration circuit 436, in one embodiment, is the physical separationof the graphics processing engines 431-432, N so that they appear to thesystem as independent units.

As mentioned, in the illustrated embodiment, one or more graphicsmemories 433-434, M are coupled to each of the graphics processingengines 431-432, N, respectively. The graphics memories 433-434, M storeinstructions and data being processed by each of the graphics processingengines 431-432, N. The graphics memories 433-434, M may be volatilememories such as DRAMs (including stacked DRAMs), GDDR memory (e.g.,GDDR5, GDDR6), or HBM, and/or may be non-volatile memories such as 3DXPoint or Nano-Ram.

In one embodiment, to reduce data traffic over the high-speed link 440,biasing techniques are used to ensure that the data stored in graphicsmemories 433-434, M is data which will be used most frequently by thegraphics processing engines 431-432, N and preferably not used by thecores 460A-460D (at least not frequently). Similarly, the biasingmechanism attempts to keep data needed by the cores (and preferably notthe graphics processing engines 431-432, N) within the caches 462A-462D,456 of the cores and system memory 411.

FIG. 4C illustrates another embodiment in which the acceleratorintegration circuit 436 is integrated within the processor 407. In thisembodiment, the graphics processing engines 431-432, N communicatedirectly over the high-speed link 440 to the accelerator integrationcircuit 436 via interface 437 and interface 435 (which, again, may beutilize any form of bus or interface protocol). The acceleratorintegration circuit 436 may perform the same operations as thosedescribed with respect to FIG. 4B, but potentially at a higherthroughput given its close proximity to the coherence bus 464 and caches462A-462D, 456.

One embodiment supports different programming models including adedicated-process programming model (no graphics acceleration modulevirtualization) and shared programming models (with virtualization). Thelatter may include programming models which are controlled by theaccelerator integration circuit 436 and programming models which arecontrolled by the graphics acceleration module 446.

In one embodiment of the dedicated process model, graphics processingengines 431-432, N are dedicated to a single application or processunder a single operating system. The single application can funnel otherapplication requests to the graphics engines 431-432, N, providingvirtualization within a VM/partition.

In the dedicated-process programming models, the graphics processingengines 431-432, N, may be shared by multiple VM/application partitions.The shared models require a system hypervisor to virtualize the graphicsprocessing engines 431-432, N to allow access by each operating system.For single-partition systems without a hypervisor, the graphicsprocessing engines 431-432, N are owned by the operating system. In bothcases, the operating system can virtualize the graphics processingengines 431-432, N to provide access to each process or application.

For the shared programming model, the graphics acceleration module 446or an individual graphics processing engine 431-432, N selects a processelement using a process handle. In one embodiment, process elements arestored in system memory 411 and are addressable using the effectiveaddress to real address translation techniques described herein. Theprocess handle may be an implementation-specific value provided to thehost process when registering its context with the graphics processingengine 431-432, N (that is, calling system software to add the processelement to the process element linked list). The lower 16-bits of theprocess handle may be the offset of the process element within theprocess element linked list.

FIG. 4D illustrates an exemplary accelerator integration slice 490. Asused herein, a “slice” comprises a specified portion of the processingresources of the accelerator integration circuit 436. Applicationeffective address space 482 within system memory 411 stores processelements 483. In one embodiment, the process elements 483 are stored inresponse to GPU invocations 481 from an application 480 executed on theprocessor 407. A process element 483 contains the process state for thecorresponding application 480. A work descriptor (WD) 484 contained inthe process element 483 can be a single job requested by an applicationor may contain a pointer to a queue of jobs. In the latter case, the WD484 is a pointer to the job request queue in the application's addressspace 482.

The graphics acceleration module 446 and/or the individual graphicsprocessing engines 431-432, N can be shared by all or a subset of theprocesses in the system. Embodiments of the invention include aninfrastructure for setting up the process state and sending a WD 484 toa graphics acceleration module 446 to start a job in a virtualizedenvironment.

In one implementation, the dedicated-process programming model isimplementation-specific. In this model, a single process owns thegraphics acceleration module 446 or an individual graphics processingengine 431. Because the graphics acceleration module 446 is owned by asingle process, the hypervisor initializes the accelerator integrationcircuit 436 for the owning partition and the operating systeminitializes the accelerator integration circuit 436 for the owningprocess at the time when the graphics acceleration module 446 isassigned.

In operation, a WD fetch unit 491 in the accelerator integration slice490 fetches the next WD 484 which includes an indication of the work tobe done by one of the graphics processing engines of the graphicsacceleration module 446. Data from the WD 484 may be stored in registers445 and used by the MMU 439, interrupt management circuit 447 and/orcontext management circuit 448 as illustrated. For example, oneembodiment of the MMU 439 includes segment/page walk circuitry foraccessing segment/page tables 486 within the OS virtual address space485. The interrupt management circuit 447 may process interrupt events492 received from the graphics acceleration module 446. When performinggraphics operations, an effective address 493 generated by a graphicsprocessing engine 431-432, N is translated to a real address by the MMU439.

In one embodiment, the same set of registers 445 are duplicated for eachgraphics processing engine 431-432, N and/or graphics accelerationmodule 446 and may be initialized by the hypervisor or operating system.Each of these duplicated registers may be included in an acceleratorintegration slice 490. Exemplary registers that may be initialized bythe hypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 RealAddress (RA) Scheduled Processes Area Pointer 3 Authority Mask OverrideRegister 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector TableEntry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA)Hypervisor Accelerator Utilization Record Pointer 9 Storage DescriptionRegister

Exemplary registers that may be initialized by the operating system areshown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and ThreadIdentification 2 Effective Address (EA) Context Save/Restore Pointer 3Virtual Address (VA) Accelerator Utilization Record Pointer 4 VirtualAddress (VA) Storage Segment Table Pointer 5 Authority Mask 6 Workdescriptor

In one embodiment, each WD 484 is specific to a particular graphicsacceleration module 446 and/or graphics processing engine 431-432, N. Itcontains all the information a graphics processing engine 431-432, Nrequires to do its work or it can be a pointer to a memory locationwhere the application has set up a command queue of work to becompleted.

FIG. 4E illustrates additional details for one embodiment of a sharedmodel. This embodiment includes a hypervisor real address space 498 inwhich a process element list 499 is stored. The hypervisor real addressspace 498 is accessible via a hypervisor 496 which virtualizes thegraphics acceleration module engines for the operating system 495.

The shared programming models allow for all or a subset of processesfrom all or a subset of partitions in the system to use a graphicsacceleration module 446. There are two programming models where thegraphics acceleration module 446 is shared by multiple processes andpartitions: time-sliced shared and graphics directed shared.

In this model, the system hypervisor 496 owns the graphics accelerationmodule 446 and makes its function available to all operating systems495. For a graphics acceleration module 446 to support virtualization bythe system hypervisor 496, the graphics acceleration module 446 mayadhere to the following requirements: 1) An application's job requestmust be autonomous (that is, the state does not need to be maintainedbetween jobs), or the graphics acceleration module 446 must provide acontext save and restore mechanism. 2) An application's job request isguaranteed by the graphics acceleration module 446 to complete in aspecified amount of time, including any translation faults, or thegraphics acceleration module 446 provides the ability to preempt theprocessing of the job. 3) The graphics acceleration module 446 must beguaranteed fairness between processes when operating in the directedshared programming model.

In one embodiment, for the shared model, the application 480 is requiredto make an operating system 495 system call with a graphics accelerationmodule 446 type, a work descriptor (WD), an authority mask register(AMR) value, and a context save/restore area pointer (CSRP). Thegraphics acceleration module 446 type describes the targetedacceleration function for the system call. The graphics accelerationmodule 446 type may be a system-specific value. The WD is formattedspecifically for the graphics acceleration module 446 and can be in theform of a graphics acceleration module 446 command, an effective addresspointer to a user-defined structure, an effective address pointer to aqueue of commands, or any other data structure to describe the work tobe done by the graphics acceleration module 446. In one embodiment, theAMR value is the AMR state to use for the current process. The valuepassed to the operating system is similar to an application setting theAMR. If the accelerator integration circuit 436 and graphicsacceleration module 446 implementations do not support a User AuthorityMask Override Register (UAMOR), the operating system may apply thecurrent UAMOR value to the AMR value before passing the AMR in thehypervisor call. The hypervisor 496 may optionally apply the currentAuthority Mask Override Register (AMOR) value before placing the AMRinto the process element 483. In one embodiment, the CSRP is one of theregisters 445 containing the effective address of an area in theapplication's address space 482 for the graphics acceleration module 446to save and restore the context state. This pointer is optional if nostate is required to be saved between jobs or when a job is preempted.The context save/restore area may be pinned system memory.

Upon receiving the system call, the operating system 495 may verify thatthe application 480 has registered and been given the authority to usethe graphics acceleration module 446. The operating system 495 thencalls the hypervisor 496 with the information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)

Upon receiving the hypervisor call, the hypervisor 496 verifies that theoperating system 495 has registered and been given the authority to usethe graphics acceleration module 446. The hypervisor 496 then puts theprocess element 483 into the process element linked list for thecorresponding graphics acceleration module 446 type. The process elementmay include the information shown in Table 4.

TABLE 4 Process Element Information 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)8 Interrupt vector table, derived from the hypervisor call parameters. 9A state register (SR) value 10 A logical partition ID (LPID) 11 A realaddress (RA) hypervisor accelerator utilization record pointer 12 TheStorage Descriptor Register (SDR)

In one embodiment, the hypervisor initializes a plurality of acceleratorintegration slice 490 registers 445.

As illustrated in FIG. 4F, one embodiment of the invention employs aunified memory addressable via a common virtual memory address spaceused to access the physical processor memories 401-402 and GPU memories420-423. In this implementation, operations executed on the GPUs 410-413utilize the same virtual/effective memory address space to access theprocessors memories 401-402 and vice versa, thereby simplifyingprogrammability. In one embodiment, a first portion of thevirtual/effective address space is allocated to the processor memory401, a second portion to the second processor memory 402, a thirdportion to the GPU memory 420, and so on. The entire virtual/effectivememory space (sometimes referred to as the effective address space) isthereby distributed across each of the processor memories 401-402 andGPU memories 420-423, allowing any processor or GPU to access anyphysical memory with a virtual address mapped to that memory.

In one embodiment, bias/coherence management circuitry 494A-494E withinone or more of the MMUs 439A-439E ensures cache coherence between thecaches of the host processors (e.g., 405) and the GPUs 410-413 andimplements biasing techniques indicating the physical memories in whichcertain types of data should be stored. While multiple instances ofbias/coherence management circuitry 494A-494E are illustrated in FIG.4F, the bias/coherence circuitry may be implemented within the MMU ofone or more host processors 405 and/or within the acceleratorintegration circuit 436.

One embodiment allows GPU-attached memory 420-423 to be mapped as partof system memory, and accessed using shared virtual memory (SVM)technology, but without suffering the typical performance drawbacksassociated with full system cache coherence. The ability to GPU-attachedmemory 420-423 to be accessed as system memory without onerous cachecoherence overhead provides a beneficial operating environment for GPUoffload. This arrangement allows the host processor 405 software tosetup operands and access computation results, without the overhead oftradition I/O DMA data copies. Such traditional copies involve drivercalls, interrupts and memory mapped I/O (MMIO) accesses that are allinefficient relative to simple memory accesses. At the same time, theability to access GPU attached memory 420-423 without cache coherenceoverheads can be critical to the execution time of an offloadedcomputation. In cases with substantial streaming write memory traffic,for example, cache coherence overhead can significantly reduce theeffective write bandwidth seen by a GPU 410-413. The efficiency ofoperand setup, the efficiency of results access, and the efficiency ofGPU computation all play a role in determining the effectiveness of GPUoffload.

In one implementation, the selection of between GPU bias and hostprocessor bias is driven by a bias tracker data structure. A bias tablemay be used, for example, which may be a page-granular structure (i.e.,controlled at the granularity of a memory page) that includes 1 or 2bits per GPU-attached memory page. The bias table may be implemented ina stolen memory range of one or more GPU-attached memories 420-423, withor without a bias cache in the GPU 410-413 (e.g., to cachefrequently/recently used entries of the bias table). Alternatively, theentire bias table may be maintained within the GPU.

In one implementation, the bias table entry associated with each accessto the GPU-attached memory 420-423 is accessed prior the actual accessto the GPU memory, causing the following operations. First, localrequests from the GPU 410-413 that find their page in GPU bias areforwarded directly to a corresponding GPU memory 420-423. Local requestsfrom the GPU that find their page in host bias are forwarded to theprocessor 405 (e.g., over a high-speed link as discussed above). In oneembodiment, requests from the processor 405 that find the requested pagein host processor bias complete the request like a normal memory read.Alternatively, requests directed to a GPU-biased page may be forwardedto the GPU 410-413. The GPU may then transition the page to a hostprocessor bias if it is not currently using the page.

The bias state of a page can be changed either by a software-basedmechanism, a hardware-assisted software-based mechanism, or, for alimited set of cases, a purely hardware-based mechanism.

One mechanism for changing the bias state employs an API call (e.g.OpenCL), which, in turn, calls the GPU's device driver which, in turn,sends a message (or enqueues a command descriptor) to the GPU directingit to change the bias state and, for some transitions, perform a cacheflushing operation in the host. The cache flushing operation is requiredfor a transition from host processor 405 bias to GPU bias, but is notrequired for the opposite transition.

In one embodiment, cache coherency is maintained by temporarilyrendering GPU-biased pages uncacheable by the host processor 405. Toaccess these pages, the processor 405 may request access from the GPU410 which may or may not grant access right away, depending on theimplementation. Thus, to reduce communication between the host processor405 and GPU 410 it is beneficial to ensure that GPU-biased pages arethose which are required by the GPU but not the host processor 405 andvice versa.

Graphics Processing Pipeline

FIG. 5 illustrates a graphics processing pipeline 500, according to anembodiment. In one embodiment a graphics processor can implement theillustrated graphics processing pipeline 500. The graphics processor canbe included within the parallel processing subsystems as describedherein, such as the parallel processor 200 of FIG. 2A, which, in oneembodiment, is a variant of the parallel processor(s) 112 of FIG. 1. Thevarious parallel processing systems can implement the graphicsprocessing pipeline 500 via one or more instances of the parallelprocessing unit (e.g., parallel processing unit 202 of FIG. 2A) asdescribed herein. For example, a shader unit (e.g., graphicsmultiprocessor 234 of FIG. 2C) may be configured to perform thefunctions of one or more of a vertex processing unit 504, a tessellationcontrol processing unit 508, a tessellation evaluation processing unit512, a geometry processing unit 516, and a fragment/pixel processingunit 524. The functions of data assembler 502, primitive assemblers 506,514, 518, tessellation unit 510, rasterizer 522, and raster operationsunit 526 may also be performed by other processing engines within aprocessing cluster (e.g., processing cluster 214 of FIG. 2A) and acorresponding partition unit (e.g., partition unit 220A-220N of FIG.2A). The graphics processing pipeline 500 may also be implemented usingdedicated processing units for one or more functions. In one embodiment,one or more portions of the graphics processing pipeline 500 can beperformed by parallel processing logic within a general purposeprocessor (e.g., CPU). In one embodiment, one or more portions of thegraphics processing pipeline 500 can access on-chip memory (e.g.,parallel processor memory 222 as in FIG. 2A) via a memory interface 528,which may be an instance of the memory interface 218 of FIG. 2A.

In one embodiment the data assembler 502 is a processing unit thatcollects vertex data for surfaces and primitives. The data assembler 502then outputs the vertex data, including the vertex attributes, to thevertex processing unit 504. The vertex processing unit 504 is aprogrammable execution unit that executes vertex shader programs,lighting and transforming vertex data as specified by the vertex shaderprograms. The vertex processing unit 504 reads data that is stored incache, local or system memory for use in processing the vertex data andmay be programmed to transform the vertex data from an object-basedcoordinate representation to a world space coordinate space or anormalized device coordinate space.

A first instance of a primitive assembler 506 receives vertex attributesfrom the vertex processing unit 504. The primitive assembler 506readings stored vertex attributes as needed and constructs graphicsprimitives for processing by tessellation control processing unit 508.The graphics primitives include triangles, line segments, points,patches, and so forth, as supported by various graphics processingapplication programming interfaces (APIs).

The tessellation control processing unit 508 treats the input verticesas control points for a geometric patch. The control points aretransformed from an input representation from the patch (e.g., thepatch's bases) to a representation that is suitable for use in surfaceevaluation by the tessellation evaluation processing unit 512. Thetessellation control processing unit 508 can also compute tessellationfactors for edges of geometric patches. A tessellation factor applies toa single edge and quantifies a view-dependent level of detail associatedwith the edge. A tessellation unit 510 is configured to receive thetessellation factors for edges of a patch and to tessellate the patchinto multiple geometric primitives such as line, triangle, orquadrilateral primitives, which are transmitted to a tessellationevaluation processing unit 512. The tessellation evaluation processingunit 512 operates on parameterized coordinates of the subdivided patchto generate a surface representation and vertex attributes for eachvertex associated with the geometric primitives.

A second instance of a primitive assembler 514 receives vertexattributes from the tessellation evaluation processing unit 512, readingstored vertex attributes as needed, and constructs graphics primitivesfor processing by the geometry processing unit 516.

The geometry processing unit 516 is a programmable execution unit thatexecutes geometry shader programs to transform graphics primitivesreceived from primitive assembler 514 as specified by the geometryshader programs. In one embodiment the geometry processing unit 516 isprogrammed to subdivide the graphics primitives into one or more newgraphics primitives and calculate parameters used to rasterize the newgraphics primitives.

In some embodiments the geometry processing unit 516 can add or deleteelements in the geometry stream. The geometry processing unit 516outputs the parameters and vertices specifying new graphics primitivesto primitive assembler 518. The primitive assembler 518 receives theparameters and vertices from the geometry processing unit 516 andconstructs graphics primitives for processing by a viewport scale, cull,and clip unit 520. The geometry processing unit 516 reads data that isstored in parallel processor memory or system memory for use inprocessing the geometry data. The viewport scale, cull, and clip unit520 performs clipping, culling, and viewport scaling and outputsprocessed graphics primitives to a rasterizer 522.

The rasterizer 522 can perform depth culling and other depth-basedoptimizations. The rasterizer 522 also performs scan conversion on thenew graphics primitives to generate fragments and output those fragmentsand associated coverage data to the fragment/pixel processing unit 524.The fragment/pixel processing unit 524 is a programmable execution unitthat is configured to execute fragment shader programs or pixel shaderprograms. The fragment/pixel processing unit 524 transforming fragmentsor pixels received from rasterizer 522, as specified by the fragment orpixel shader programs. For example, the fragment/pixel processing unit524 may be programmed to perform operations included but not limited totexture mapping, shading, blending, texture correction and perspectivecorrection to produce shaded fragments or pixels that are output to araster operations unit 526. The fragment/pixel processing unit 524 canread data that is stored in either the parallel processor memory or thesystem memory for use when processing the fragment data. Fragment orpixel shader programs may be configured to shade at sample, pixel, tile,or other granularities depending on the sampling rate configured for theprocessing units.

The raster operations unit 526 is a processing unit that performs rasteroperations including, but not limited to stencil, z-test, blending, andthe like, and outputs pixel data as processed graphics data to be storedin graphics memory (e.g., parallel processor memory 222 as in FIG. 2A,and/or system memory 104 as in FIG. 1), to be displayed on the one ormore display device(s) 110 or for further processing by one of the oneor more processor(s) 102 or parallel processor(s) 112. In someembodiments the raster operations unit 526 is configured to compress zor color data that is written to memory and decompress z or color datathat is read from memory.

Machine Learning Overview

The architecture described above can be applied to perform training andinference operations using machine learning models. Machine learning hasbeen successful at solving many kinds of tasks. The computations thatarise when training and using machine learning algorithms (e.g., neuralnetworks) lend themselves naturally to efficient parallelimplementations. Accordingly, parallel processors such asgeneral-purpose graphic processing units (GPGPUs) have played asignificant role in the practical implementation of deep neuralnetworks. Parallel graphics processors with single instruction, multiplethread (SIMT) architectures are designed to maximize the amount ofparallel processing in the graphics pipeline. In an SIMT architecture,groups of parallel threads attempt to execute program instructionssynchronously together as often as possible to increase processingefficiency. The efficiency provided by parallel machine learningalgorithm implementations allows the use of high capacity networks andenables those networks to be trained on larger datasets.

A machine learning algorithm is an algorithm that can learn based on aset of data. Embodiments of machine learning algorithms can be designedto model high-level abstractions within a data set. For example, imagerecognition algorithms can be used to determine which of severalcategories to which a given input belong; regression algorithms canoutput a numerical value given an input; and pattern recognitionalgorithms can be used to generate translated text or perform text tospeech and/or speech recognition.

An exemplary type of machine learning algorithm is a neural network.There are many types of neural networks; a simple type of neural networkis a feedforward network. A feedforward network may be implemented as anacyclic graph in which the nodes are arranged in layers. Typically, afeedforward network topology includes an input layer and an output layerthat are separated by at least one hidden layer. The hidden layertransforms input received by the input layer into a representation thatis useful for generating output in the output layer. The network nodesare fully connected via edges to the nodes in adjacent layers, but thereare no edges between nodes within each layer. Data received at the nodesof an input layer of a feedforward network are propagated (i.e., “fedforward”) to the nodes of the output layer via an activation functionthat calculates the states of the nodes of each successive layer in thenetwork based on coefficients (“weights”) respectively associated witheach of the edges connecting the layers. Depending on the specific modelbeing represented by the algorithm being executed, the output from theneural network algorithm can take various forms.

Before a machine learning algorithm can be used to model a particularproblem, the algorithm is trained using a training data set. Training aneural network involves selecting a network topology, using a set oftraining data representing a problem being modeled by the network, andadjusting the weights until the network model performs with a minimalerror for all instances of the training data set. For example, during asupervised learning training process for a neural network, the outputproduced by the network in response to the input representing aninstance in a training data set is compared to the “correct” labeledoutput for that instance, an error signal representing the differencebetween the output and the labeled output is calculated, and the weightsassociated with the connections are adjusted to minimize that error asthe error signal is backward propagated through the layers of thenetwork. The network is considered “trained” when the errors for each ofthe outputs generated from the instances of the training data set areminimized.

The accuracy of a machine learning algorithm can be affectedsignificantly by the quality of the data set used to train thealgorithm. The training process can be computationally intensive and mayrequire a significant amount of time on a conventional general-purposeprocessor. Accordingly, parallel processing hardware is used to trainmany types of machine learning algorithms. This is particularly usefulfor optimizing the training of neural networks, as the computationsperformed in adjusting the coefficients in neural networks lendthemselves naturally to parallel implementations. Specifically, manymachine learning algorithms and software applications have been adaptedto make use of the parallel processing hardware within general-purposegraphics processing devices.

FIG. 6 is a generalized diagram of a machine learning software stack600. A machine learning application 602 can be configured to train aneural network using a training dataset or to use a trained deep neuralnetwork to implement machine intelligence. The machine learningapplication 602 can include training and inference functionality for aneural network and/or specialized software that can be used to train aneural network before deployment. The machine learning application 602can implement any type of machine intelligence including but not limitedto image recognition, mapping and localization, autonomous navigation,speech synthesis, medical imaging, or language translation.

Hardware acceleration for the machine learning application 602 can beenabled via a machine learning framework 604. The machine learningframework 604 can provide a library of machine learning primitives.Machine learning primitives are basic operations that are commonlyperformed by machine learning algorithms. Without the machine learningframework 604, developers of machine learning algorithms would berequired to create and optimize the main computational logic associatedwith the machine learning algorithm, then re-optimize the computationallogic as new parallel processors are developed. Instead, the machinelearning application can be configured to perform the necessarycomputations using the primitives provided by the machine learningframework 604. Exemplary primitives include tensor convolutions,activation functions, and pooling, which are computational operationsthat are performed while training a convolutional neural network (CNN).The machine learning framework 604 can also provide primitives toimplement basic linear algebra subprograms performed by manymachine-learning algorithms, such as matrix and vector operations.

The machine learning framework 604 can process input data received fromthe machine learning application 602 and generate the appropriate inputto a compute framework 606. The compute framework 606 can abstract theunderlying instructions provided to the GPGPU driver 608 to enable themachine learning framework 604 to take advantage of hardwareacceleration via the GPGPU hardware 610 without requiring the machinelearning framework 604 to have intimate knowledge of the architecture ofthe GPGPU hardware 610. Additionally, the compute framework 606 canenable hardware acceleration for the machine learning framework 604across a variety of types and generations of the GPGPU hardware 610.

GPGPU Machine Learning Acceleration

FIG. 7 illustrates a general-purpose graphics processing unit 700,according to an embodiment. In one embodiment, the general-purposeprocessing unit (GPGPU) 700 can be configured to be particularlyefficient in processing the type of computational workloads associatedwith training deep neural networks. Additionally, the GPGPU 700 can belinked directly to other instances of the GPGPU to create a multi-GPUcluster to improve training speed for particularly deep neural networks.

The GPGPU 700 includes a host interface 702 to enable a connection witha host processor. In one embodiment the host interface 702 is a PCIExpress interface. However, the host interface can also be a vendorspecific communications interface or communications fabric. The GPGPU700 receives commands from the host processor and uses a globalscheduler 704 to distribute execution threads associated with thosecommands to a set of compute clusters 706A-706H. The compute clusters706A-706H share a cache memory 708. The cache memory 708 can serve as ahigher-level cache for cache memories within the compute clusters706A-706H.

The GPGPU 700 includes memory 714A-B coupled with the compute clusters706A-H via a set of memory controllers 712A-712B. In variousembodiments, the memory 714A-714B can include various types of memorydevices including dynamic random-access memory (DRAM) or graphics randomaccess memory, such as synchronous graphics random access memory(SGRAM), including graphics double data rate (GDDR) memory. In oneembodiment, the memory 714A-714N may also include 3D stacked memory,including but not limited to high bandwidth memory (HBM).

In one embodiment, each of the compute clusters 706A-706H includes a setof graphics multiprocessors, such as the graphics multiprocessor 400 ofFIG. 4A. The graphics multiprocessors of the compute cluster multipletypes of integer and floating-point logic units that can performcomputational operations at a range of precisions including suited formachine learning computations. For example, and in one embodiment atleast a subset of the floating-point units in each of the computeclusters 706A-H can be configured to perform 16-bit or 32-bit floatingpoint operations, while a different subset of the floating-point unitscan be configured to perform 64-bit floating point operations.

Multiple instances of the GPGPU 700 can be configured to operate as acompute cluster. The communication mechanism used by the compute clusterfor synchronization and data exchange varies across embodiments. In oneembodiment, the multiple instances of the GPGPU 700 communicate over thehost interface 702. In one embodiment the GPGPU 700 includes an I/O hub709 that couples the GPGPU 700 with a GPU link 710 that enables a directconnection to other instances of the GPGPU. In one embodiment the GPUlink 710 is coupled to a dedicated GPU-to-GPU bridge that enablescommunication and synchronization between multiple instances of theGPGPU 700. In one embodiment the GPU link 710 couples with a high-speedinterconnect to transmit and receive data to other GPGPUs or parallelprocessors. In one embodiment the multiple instances of the GPGPU 700are located in separate data processing systems and communicate via anetwork device that is accessible via the host interface 702. In oneembodiment the GPU link 710 can be configured to enable a connection toa host processor in addition to or as an alternative to the hostinterface 702.

While the illustrated configuration of the GPGPU 700 can be configuredto train neural networks, one embodiment provides alternateconfiguration of the GPGPU 700 that can be configured for deploymentwithin a high performance or low power inferencing platform. In aninferencing configuration, the GPGPU 700 includes fewer of the computeclusters 706A-706H relative to the training configuration. Additionally,memory technology associated with the memory 714A-714B may differbetween inferencing and training configurations. In one embodiment, theinferencing configuration of the GPGPU 700 can support inferencingspecific instructions. For example, an inferencing configuration canprovide support for one or more 8-bit integer dot product instructions,which are commonly used during inferencing operations for deployedneural networks.

FIG. 8 illustrates a multi-GPU computing system 800, according to anembodiment. The multi-GPU computing system 800 can include a processor802 coupled to multiple GPGPUs 806A-806D via a host interface switch804. The host interface switch 804, in one embodiment, is a PCI expressswitch device that couples the processor 802 to a PCI express bus overwhich the processor 802 can communicate with the set of GPGPUs806A-806D. Each of the multiple GPGPUs 806A-806D can be an instance ofthe GPGPU 700 of FIG. 7. The GPGPUs 806A-806D can interconnect via a setof high-speed point to point GPU to GPU links 816. The high-speed GPU toGPU links can connect to each of the GPGPUs 806A-806D via a dedicatedGPU link, such as the GPU link 710 as in FIG. 7. The P2P GPU links 816enable direct communication between each of the GPGPUs 806A-806D withoutrequiring communication over the host interface bus to which theprocessor 802 is connected. With GPU-to-GPU traffic directed to the P2PGPU links, the host interface bus remains available for system memoryaccess or to communicate with other instances of the multi-GPU computingsystem 800, for example, via one or more network devices. While in theillustrated embodiment the GPGPUs 806A-D connect to the processor 802via the host interface switch 804, in one embodiment the processor 802includes direct support for the P2P GPU links 816 and can connectdirectly to the GPGPUs 806A-806D.

Machine Learning Neural Network Implementations

The computing architecture provided by embodiments described herein canbe configured to perform the types of parallel processing that isparticularly suited for training and deploying neural networks formachine learning. A neural network can be generalized as a network offunctions having a graph relationship. As is well-known in the art,there are a variety of types of neural network implementations used inmachine learning. One exemplary type of neural network is thefeedforward network, as previously described.

A second exemplary type of neural network is the Convolutional NeuralNetwork (CNN). A CNN is a specialized feedforward neural network forprocessing data having a known, grid-like topology, such as image data.Accordingly, CNNs are commonly used for compute vision and imagerecognition applications, but they also may be used for other types ofpattern recognition such as speech and language processing. The nodes inthe CNN input layer are organized into a set of “filters” (featuredetectors inspired by the receptive fields found in the retina), and theoutput of each set of filters is propagated to nodes in successivelayers of the network. The computations for a CNN include applying theconvolution mathematical operation to each filter to produce the outputof that filter. Convolution is a specialized kind of mathematicaloperation performed by two functions to produce a third function that isa modified version of one of the two original functions. Inconvolutional network terminology, the first function to the convolutioncan be referred to as the input, while the second function can bereferred to as the convolution kernel. The output may be referred to asthe feature map. For example, the input to a convolution layer can be amultidimensional array of data that defines the various color componentsof an input image. The convolution kernel can be a multidimensionalarray of parameters, where the parameters are adapted by the trainingprocess for the neural network.

Recurrent neural networks (RNNs) are a family of feedforward neuralnetworks that include feedback connections between layers. RNNs enablemodeling of sequential data by sharing parameter data across differentparts of the neural network. The architecture for a RNN includes cycles.The cycles represent the influence of a present value of a variable onits own value at a future time, as at least a portion of the output datafrom the RNN is used as feedback for processing subsequent input in asequence. This feature makes RNNs particularly useful for languageprocessing due to the variable nature in which language data can becomposed.

The figures described below present exemplary feedforward, CNN, and RNNnetworks, as well as describe a general process for respectivelytraining and deploying each of those types of networks. It will beunderstood that these descriptions are exemplary and non-limiting as toany specific embodiment described herein and the concepts illustratedcan be applied generally to deep neural networks and machine learningtechniques in general.

The exemplary neural networks described above can be used to performdeep learning. Deep learning is machine learning using deep neuralnetworks. The deep neural networks used in deep learning are artificialneural networks composed of multiple hidden layers, as opposed toshallow neural networks that include only a single hidden layer. Deeperneural networks are generally more computationally intensive to train.However, the additional hidden layers of the network enable multisteppattern recognition that results in reduced output error relative toshallow machine learning techniques.

Deep neural networks used in deep learning typically include a front-endnetwork to perform feature recognition coupled to a back-end networkwhich represents a mathematical model that can perform operations (e.g.,object classification, speech recognition, etc.) based on the featurerepresentation provided to the model. Deep learning enables machinelearning to be performed without requiring hand crafted featureengineering to be performed for the model. Instead, deep neural networkscan learn features based on statistical structure or correlation withinthe input data. The learned features can be provided to a mathematicalmodel that can map detected features to an output. The mathematicalmodel used by the network is generally specialized for the specific taskto be performed, and different models will be used to perform differenttask.

Once the neural network is structured, a learning model can be appliedto the network to train the network to perform specific tasks. Thelearning model describes how to adjust the weights within the model toreduce the output error of the network. Backpropagation of errors is acommon method used to train neural networks. An input vector ispresented to the network for processing. The output of the network iscompared to the desired output using a loss function and an error valueis calculated for each of the neurons in the output layer. The errorvalues are then propagated backwards until each neuron has an associatederror value which roughly represents its contribution to the originaloutput. The network can then learn from those errors using an algorithm,such as the stochastic gradient descent algorithm, to update the weightsof the of the neural network.

FIG. 9A-9B illustrate an exemplary convolutional neural network. FIG. 9Aillustrates various layers within a CNN. As shown in FIG. 9A, anexemplary CNN used to model image processing can receive input 902describing the red, green, and blue (RGB) components of an input image.The input 902 can be processed by multiple convolutional layers (e.g.,convolutional layer 904, convolutional layer 906). The output from themultiple convolutional layers may optionally be processed by a set offully connected layers 908. Neurons in a fully connected layer have fullconnections to all activations in the previous layer, as previouslydescribed for a feedforward network. The output from the fully connectedlayers 908 can be used to generate an output result from the network.The activations within the fully connected layers 908 can be computedusing matrix multiplication instead of convolution. Not all CNNimplementations make use of fully connected layers 908. For example, insome implementations the convolutional layer 906 can generate output forthe CNN.

The convolutional layers are sparsely connected, which differs fromtraditional neural network configuration found in the fully connectedlayers 908. Traditional neural network layers are fully connected, suchthat every output unit interacts with every input unit. However, theconvolutional layers are sparsely connected because the output of theconvolution of a field is input (instead of the respective state valueof each of the nodes in the field) to the nodes of the subsequent layer,as illustrated. The kernels associated with the convolutional layersperform convolution operations, the output of which is sent to the nextlayer. The dimensionality reduction performed within the convolutionallayers is one aspect that enables the CNN to scale to process largeimages.

FIG. 9B illustrates exemplary computation stages within a convolutionallayer of a CNN. Input to a convolutional layer 912 of a CNN can beprocessed in three stages of a convolutional layer 914. The three stagescan include a convolution stage 916, a detector stage 918, and a poolingstage 920. The convolution layer 914 can then output data to asuccessive convolutional layer. The final convolutional layer of thenetwork can generate output feature map data or provide input to a fullyconnected layer, for example, to generate a classification value for theinput to the CNN.

In the convolution stage 916 performs several convolutions in parallelto produce a set of linear activations. The convolution stage 916 caninclude an affine transformation, which is any transformation that canbe specified as a linear transformation plus a translation. Affinetransformations include rotations, translations, scaling, andcombinations of these transformations. The convolution stage computesthe output of functions (e.g., neurons) that are connected to specificregions in the input, which can be determined as the local regionassociated with the neuron. The neurons compute a dot product betweenthe weights of the neurons and the region in the local input to whichthe neurons are connected. The output from the convolution stage 916defines a set of linear activations that are processed by successivestages of the convolutional layer 914.

The linear activations can be processed by a detector stage 918. In thedetector stage 918, each linear activation is processed by a non-linearactivation function. The non-linear activation function increases thenonlinear properties of the overall network without affecting thereceptive fields of the convolution layer. Several types of non-linearactivation functions may be used. One particular type is the rectifiedlinear unit (ReLU), which uses an activation function defined asƒ(x)=max (0, x), such that the activation is thresholded at zero.

The pooling stage 920 uses a pooling function that replaces the outputof the convolutional layer 906 with a summary statistic of the nearbyoutputs. The pooling function can be used to introduce translationinvariance into the neural network, such that small translations to theinput do not change the pooled outputs. Invariance to local translationcan be useful in scenarios where the presence of a feature in the inputdata is more important than the precise location of the feature. Varioustypes of pooling functions can be used during the pooling stage 920,including max pooling, average pooling, and 12-norm pooling.Additionally, some CNN implementations do not include a pooling stage.Instead, such implementations substitute and additional convolutionstage having an increased stride relative to previous convolutionstages.

The output from the convolutional layer 914 can then be processed by thenext layer 922. The next layer 922 can be an additional convolutionallayer or one of the fully connected layers 908. For example, the firstconvolutional layer 904 of FIG. 9A can output to the secondconvolutional layer 906, while the second convolutional layer can outputto a first layer of the fully connected layers 908.

FIG. 10 illustrates an exemplary recurrent neural network 1000. In arecurrent neural network (RNN), the previous state of the networkinfluences the output of the current state of the network. RNNs can bebuilt in a variety of ways using a variety of functions. The use of RNNsgenerally revolves around using mathematical models to predict thefuture based on a prior sequence of inputs. For example, an RNN may beused to perform statistical language modeling to predict an upcomingword given a previous sequence of words. The illustrated RNN 1000 can bedescribed has having an input layer 1002 that receives an input vector,hidden layers 1004 to implement a recurrent function, a feedbackmechanism 1005 to enable a ‘memory’ of previous states, and an outputlayer 1006 to output a result. The RNN 1000 operates based ontime-steps. The state of the RNN at a given time step is influencedbased on the previous time step via the feedback mechanism 1005. For agiven time step, the state of the hidden layers 1004 is defined by theprevious state and the input at the current time step. An initial input(x₁) at a first time step can be processed by the hidden layer 1004. Asecond input (x₂) can be processed by the hidden layer 1004 using stateinformation that is determined during the processing of the initialinput (x₁). A given state can be computed as s_(t)=ƒ(Ux_(t)+Ws_(t-1)),where U and W are parameter matrices. The function ƒ is generally anonlinearity, such as the hyperbolic tangent function (Tanh) or avariant of the rectifier function ƒ (x)=max(0, x). However, the specificmathematical function used in the hidden layers 1004 can vary dependingon the specific implementation details of the RNN 1000.

In addition to the basic CNN and RNN networks described, variations onthose networks may be enabled. One example RNN variant is the long shortterm memory (LSTM) RNN. LSTM RNNs are capable of learning long-termdependencies that may be necessary for processing longer sequences oflanguage. A variant on the CNN is a convolutional deep belief network,which has a structure similar to a CNN and is trained in a mannersimilar to a deep belief network. A deep belief network (DBN) is agenerative neural network that is composed of multiple layers ofstochastic (random) variables. DBNs can be trained layer-by-layer usinggreedy unsupervised learning. The learned weights of the DBN can then beused to provide pre-train neural networks by determining an optimalinitial set of weights for the neural network.

FIG. 11 illustrates training and deployment of a deep neural network.Once a given network has been structured for a task the neural networkis trained using a training dataset 1102. Various training frameworks1104 have been developed to enable hardware acceleration of the trainingprocess. For example, the machine learning framework 604 of FIG. 6 maybe configured as a training framework 604. The training framework 604can hook into an untrained neural network 1106 and enable the untrainedneural net to be trained using the parallel processing resourcesdescribed herein to generate a trained neural network 1108.

To start the training process the initial weights may be chosen randomlyor by pre-training using a deep belief network. The training cycle thenbe performed in either a supervised or unsupervised manner.

Supervised learning is a learning method in which training is performedas a mediated operation, such as when the training dataset 1102 includesinput paired with the desired output for the input, or where thetraining dataset includes input having known output and the output ofthe neural network is manually graded. The network processes the inputsand compares the resulting outputs against a set of expected or desiredoutputs. Errors are then propagated back through the system. Thetraining framework 1104 can adjust to adjust the weights that controlthe untrained neural network 1106. The training framework 1104 canprovide tools to monitor how well the untrained neural network 1106 isconverging towards a model suitable to generating correct answers basedon known input data. The training process occurs repeatedly as theweights of the network are adjusted to refine the output generated bythe neural network. The training process can continue until the neuralnetwork reaches a statistically desired accuracy associated with atrained neural network 1108. The trained neural network 1108 can then bedeployed to implement any number of machine learning operations togenerate an inference result 1114 based on input of new data 1112.

Unsupervised learning is a learning method in which the network attemptsto train itself using unlabeled data. Thus, for unsupervised learningthe training dataset 1102 will include input data without any associatedoutput data. The untrained neural network 1106 can learn groupingswithin the unlabeled input and can determine how individual inputs arerelated to the overall dataset. Unsupervised training can be used togenerate a self-organizing map, which is a type of trained neuralnetwork 1108 capable of performing operations useful in reducing thedimensionality of data. Unsupervised training can also be used toperform anomaly detection, which allows the identification of datapoints in an input dataset that deviate from the normal patterns of thedata.

Variations on supervised and unsupervised training may also be employed.Semi-supervised learning is a technique in which in the training dataset1102 includes a mix of labeled and unlabeled data of the samedistribution. Incremental learning is a variant of supervised learningin which input data is continuously used to further train the model.Incremental learning enables the trained neural network 1108 to adapt tothe new data 1112 without forgetting the knowledge instilled within thenetwork during initial training.

Whether supervised or unsupervised, the training process forparticularly deep neural networks may be too computationally intensivefor a single compute node. Instead of using a single compute node, adistributed network of computational nodes can be used to accelerate thetraining process.

FIG. 12 is a block diagram illustrating distributed learning.Distributed learning is a training model that uses multiple distributedcomputing nodes to perform supervised or unsupervised training of aneural network. The distributed computational nodes can each include oneor more host processors and one or more of the general-purposeprocessing nodes, such as the highly parallel general-purpose graphicsprocessing unit 700 as in FIG. 7. As illustrated, distributed learningcan be performed model parallelism 1202, data parallelism 1204, or acombination of model and data parallelism 1204.

In model parallelism 1202, different computational nodes in adistributed system can perform training computations for different partsof a single network. For example, each layer of a neural network can betrained by a different processing node of the distributed system. Thebenefits of model parallelism include the ability to scale toparticularly large models. Splitting the computations associated withdifferent layers of the neural network enables the training of verylarge neural networks in which the weights of all layers would not fitinto the memory of a single computational node. In some instances, modelparallelism can be particularly useful in performing unsupervisedtraining of large neural networks.

In data parallelism 1204, the different nodes of the distributed networkhave a complete instance of the model and each node receives a differentportion of the data. The results from the different nodes are thencombined. While different approaches to data parallelism are possible,data parallel training approaches all require a technique of combiningresults and synchronizing the model parameters between each node.Exemplary approaches to combining data include parameter averaging andupdate based data parallelism. Parameter averaging trains each node on asubset of the training data and sets the global parameters (e.g.,weights, biases) to the average of the parameters from each node.Parameter averaging uses a central parameter server that maintains theparameter data. Update based data parallelism is similar to parameteraveraging except that instead of transferring parameters from the nodesto the parameter server, the updates to the model are transferred.Additionally, update based data parallelism can be performed in adecentralized manner, where the updates are compressed and transferredbetween nodes.

Combined model and data parallelism 1206 can be implemented, forexample, in a distributed system in which each computational nodeincludes multiple GPUs. Each node can have a complete instance of themodel with separate GPUs within each node are used to train differentportions of the model.

Distributed training has increased overhead relative to training on asingle machine. However, the parallel processors and GPGPUs describedherein can each implement various techniques to reduce the overhead ofdistributed training, including techniques to enable high bandwidthGPU-to-GPU data transfer and accelerated remote data synchronization.

Exemplary Machine Learning Applications

Machine learning can be applied to solve a variety of technologicalproblems, including but not limited to computer vision, autonomousdriving and navigation, speech recognition, and language processing.Computer vision has traditionally been one of the most active researchareas for machine learning applications. Applications of computer visionrange from reproducing human visual abilities, such as recognizingfaces, to creating new categories of visual abilities. For example,computer vision applications can be configured to recognize sound wavesfrom the vibrations induced in objects visible in a video. Parallelprocessor accelerated machine learning enables computer visionapplications to be trained using significantly larger training datasetthan previously feasible and enables inferencing systems to be deployedusing low power parallel processors.

Parallel processor accelerated machine learning has autonomous drivingapplications including lane and road sign recognition, obstacleavoidance, navigation, and driving control. Accelerated machine learningtechniques can be used to train driving models based on datasets thatdefine the appropriate responses to specific training input. Theparallel processors described herein can enable rapid training of theincreasingly complex neural networks used for autonomous drivingsolutions and enables the deployment of low power inferencing processorsin a mobile platform suitable for integration into autonomous vehicles.

Parallel processor accelerated deep neural networks have enabled machinelearning approaches to automatic speech recognition (ASR). ASR includesthe creation of a function that computes the most probable linguisticsequence given an input acoustic sequence. Accelerated machine learningusing deep neural networks have enabled the replacement of the hiddenMarkov models (HMMs) and Gaussian mixture models (GMMs) previously usedfor ASR.

Parallel processor accelerated machine learning can also be used toaccelerate natural language processing. Automatic learning procedurescan make use of statistical inference algorithms to produce models thatare robust to erroneous or unfamiliar input. Exemplary natural languageprocessor applications include automatic machine translation betweenhuman languages.

The parallel processing platforms used for machine learning can bedivided into training platforms and deployment platforms. Trainingplatforms are generally highly parallel and include optimizations toaccelerate multi-GPU single node training and multi-node, multi-GPUtraining. Exemplary parallel processors suited for training include thegeneral-purpose graphics processing unit 700 of FIG. 7 and the multi-GPUcomputing system 800 of FIG. 8. On the contrary, deployed machinelearning platforms generally include lower power parallel processorssuitable for use in products such as cameras, autonomous robots, andautonomous vehicles.

FIG. 13 illustrates an exemplary inferencing system on a chip (SOC) 1300suitable for performing inferencing using a trained model. The SOC 1300can integrate processing components including a media processor 1302, avision processor 1304, a GPGPU 1306 and a multi-core processor 1308. TheSOC 1300 can additionally include on-chip memory 1305 that can enable ashared on-chip data pool that is accessible by each of the processingcomponents. The processing components can be optimized for low poweroperation to enable deployment to a variety of machine learningplatforms, including autonomous vehicles and autonomous robots. Forexample, one implementation of the SOC 1300 can be used as a portion ofthe main control system for an autonomous vehicle. Where the SOC 1300 isconfigured for use in autonomous vehicles the SOC is designed andconfigured for compliance with the relevant functional safety standardsof the deployment jurisdiction.

During operation, the media processor 1302 and vision processor 1304 canwork in concert to accelerate computer vision operations. The mediaprocessor 1302 can enable low latency decode of multiple high-resolution(e.g., 4K, 8K) video streams. The decoded video streams can be writtento a buffer in the on-chip memory 1305. The vision processor 1304 canthen parse the decoded video and perform preliminary processingoperations on the frames of the decoded video in preparation ofprocessing the frames using a trained image recognition model. Forexample, the vision processor 1304 can accelerate convolution operationsfor a CNN that is used to perform image recognition on thehigh-resolution video data, while back-end model computations areperformed by the GPGPU 1306.

The multi-core processor 1308 can include control logic to assist withsequencing and synchronization of data transfers and shared memoryoperations performed by the media processor 1302 and the visionprocessor 1304. The multi-core processor 1308 can also function as anapplication processor to execute software applications that can make useof the inferencing compute capability of the GPGPU 1306. For example, atleast a portion of the navigation and driving logic can be implementedin software executing on the multi-core processor 1308. Such softwarecan directly issue computational workloads to the GPGPU 1306 or thecomputational workloads can be issued to the multi-core processor 1308,which can offload at least a portion of those operations to the GPGPU1306.

The GPGPU 1306 can include compute clusters such as a low powerconfiguration of the compute clusters 706A-706H within general-purposegraphics processing unit 700. The compute clusters within the GPGPU 1306can support instruction that are specifically optimized to performinferencing computations on a trained neural network. For example, theGPGPU 1306 can support instructions to perform low precisioncomputations such as 8-bit and 4-bit integer vector operations.

Local Memory Sharing Between Kernels

GPU compute APIs such as CUDA and OpenCL allow a programmer to targetdifferent classes of memory for data storage. Parallel functions (e.g.,kernels) that execute on a CUDA or OpenCL device can make use of globalmemory, which is the GPU device memory. Kernels can also request on-chipmemory, such as local memory in OpenCL or shared memory in CUDA. Suchallocations may be collectively referred to as shared local memory.Kernels are launched on GPUs with a specified number of threads or workitems (e.g., NDRange, grid size) and a specified number of threads orwork items per block. Thread blocks may then be divided intoarchitecture-specific groups (e.g., work groups, warps, wavefronts,etc.) that are distributed to compute units, execution units, ormultiprocessors within the GPU for parallel execution. While GPUs aregenerally referred to herein, the techniques described are generallyapplicable to parallel processors in general, including fieldprogrammable gate arrays (FPGAs) and digital signal processors equippedwith on-chip memory.

All threads within a work group have access to shared local memory.However, shared local memory is flushed and cleared between thread groupruns. While it may be beneficial under some circumstances for kernelthreads to begin with initialized shared local memory, clearing sharedlocal memory between thread groups prevents the use of programmingmodels in which a kernel is dependent upon or makes use of resultswritten to shared local memory by previous kernels. Embodimentsdescribed herein provide hardware and software mechanisms to enable thesharing of shared local between kernel calls. In one embodiment, usingidentifiers within a thread grid to create a dependency graph betweenkernels. Kernels having a dependency relationship can be executed backto back without clearing SLM. In a further embodiment, a hardwarescheduler can be configured to enable the simultaneous execution ofdifferent kernels within the same dependency list, where dependency canbe determined and enforced via thread group ID.

Sharing local memory between kernels can increase the efficiency ofcomputing activations for a neural network. Conventional techniques forcomputing activations for neural network layers is to write data toglobal memory and use that data in the next neural network layer. Thenext layer can call a new kernel and load the activations from globalmemory. Techniques described herein enable kernels to store activationsin shared local memory and make the next layer kernels depend on theprevious kernels. The activations can then be read from shared localmemory instead of global memory, reducing traffic and improving accesslatency. These techniques can also be applied to high-performancecomputing applications, where successive kernels can share data viashared local memory.

FIG. 14 is a block diagram of a data processing system 1400, accordingto an embodiment. The data processing system 1400 is a heterogeneousprocessing system having a processor 1402, unified memory 1410, and aGPGPU 1420 including machine learning acceleration logic. The processor1402 and the GPGPU 1420 can be any of the processors and GPGPU/parallelprocessors as described herein. The processor 1402 can executeinstructions for a compiler 1415 stored in system memory 1412. Thecompiler 1415 executes on the processor 1402 to compile source code1414A into compiled code 1414B. The compiled code 1414B can includeinstructions that may be executed by the processor 1402 and/orinstructions that may be executed by the GPGPU 1420. During compilation,the compiler 1415 can perform operations to insert metadata, includinghints as to the level of data parallelism present in the compiled code1414B and/or hints regarding the data locality associated with threadsto be dispatched based on the compiled code 1414B. The compiler 1415 caninclude the information necessary to perform such operations or theoperations can be performed with the assistance of a runtime library1416. The runtime library 1416 can also assist the compiler 1415 in thecompilation of the source code 1414A and can also include instructionsthat are linked at runtime with the compiled code 1414B to facilitateexecution of the compiled instructions on the GPGPU 1420.

The unified memory 1410 represents a unified address space that may beaccessed by the processor 1402 and the GPGPU 1420. The unified memorycan include system memory 1412 as well as GPGPU memory 1418. The GPGPUmemory 1418 is memory within an address pace of the GPGPU 1420 and caninclude some or all of system memory 1412. In one embodiment the GPGPUmemory 1418 can also include at least a portion of any memory dedicatedfor use exclusively by the GPGPU 1420, such as a GPGPU local memory1428. In one embodiment, compiled code 1414B stored in system memory1412 can be mapped into GPGPU memory 1418 for access by the GPGPU 1420.

The GPGPU 1420 includes multiple engine block partitions 1424A-1424N,which can include one or more of a variety of compute units or executionelements described herein. In one embodiment the GPGPU 1420 additionallyincludes a matrix accelerator 1423, which can include one or morespecial function compute units that are designed to accelerate a subsetof matrix operations (e.g., dot product, etc.). The GPGPU 1420 can alsoinclude a set of resources that can be shared by the engine blockpartitions 1424A-1424N, including but not limited to a set of globalregisters 1425, a power and performance unit 1426, and a shared cache1427. In one embodiment the global registers 1425 include directly andindirectly accessible registers. The power and performance unit 1426 canbe configured to adjust power delivery and clock frequencies for theengine block partitions 1424A-1424N to power gate idle components withinthe engine block partitions 1424A-1424N. In various embodiments theshared cache 1427 can include an instruction cache and/or a lower leveldata cache.

The GPGPU 1420 can also include a GPGPU local memory 1428. The GPGPUlocal memory 1428 can make up a portion of the unified memory 1410,along with any system memory 1412 that is mapped into GPGPU memory 1418.The GPGPU local memory 1428 can include one or more of a variety ofgraphics processor memory technologies, including but not limited to HBMand GDDR memories.

In one embodiment, each engine block partition 1424A-1424N includes aset of graphics processing engines that can operated independently or inconcert to execute multiple workloads or a single distributed workload.Each partition includes a variety of engines that perform differentactivities. The variety of engines can process commands provided withina command buffer and execute those commands using execution units withinthe engine block partition 1424A-1424N. Software that executes on a hostprocessor can submit work items to the global scheduler 1422, which candistribute the various work items to one or more engine block partitions1424A-1424N. Alternatively, the software can submit work items directlyto a partition and scheduling hardware within the partition can schedulethe workload to the appropriate engine within the partition.

FIG. 15 illustrates an engine block partition 1424 of a GPGPU, accordingto an embodiment. The engine block partition 1424 includes a sharedinstruction cache 1502, a texture unit 1518, and a cache/shared memory1520 that are common to the execution resources within the engine blockpartition 1424. The cache/shared memory 1520 can be dynamicallypartitioned between an implicitly managed cache memory and an explicitlymanaged shared local memory. The engine block partition 1424 can includemultiple sub-partitions 1501A-1501N and a graphics processor can includemultiple instances of the engine block partition 1424, as shown in FIG.14. The sub-partitions 1501A-1501N can include support logic including alocal instruction cache 1504A-1504N, a thread scheduler 1506A-1506N, athread dispatcher 1508A-1508N, and a set of registers 1510A. To performlogic operations, the sub-partitions 1501A-1501N can include a set ofadditional function units (AFUs 1512A-1512N), floating-point units (FPU1514A-1514N), integer arithmetic logic units (ALUs 1516-1516N), addresscomputational units (ACU 1513A-1513N), double-precision floating-pointunits (DPFPU 1515A-1515N), and matrix processing units (MPU1517A-1517N). The additional function units can be configured to performa variety of operations, including but not limited to ray-tracingoperations. Any number of sub-partitions 1501-1501N may be present(e.g., 2, 4, 16, etc.). In one embodiment, the engine block partition1424 is configured as a streaming multiprocessor, in which a largenumber of threads may be executed in a streaming manner. The engineblock partition 1424 can also be a tile of GPU engines within a tiledGPU architecture.

Some of the computational units operate at a specific precision. Forexample, the FPUs 1514A-1514N can perform single-precision (32-bit) andhalf-precision (16-bit) floating point operations, while the DPFPUs1515A-1515N perform double precision (144-bit) floating pointoperations. The ALUs 1516A-1516N can perform variable precision integeroperations at 4-bit, 16-bit, 16-bit, and 32-bit precision, and can beconfigured for mixed precision operations. The MPUs 1517A-1517N can alsobe configured for mixed precision matrix operations, includinghalf-precision floating point and 16-bit integer operations. The MPUs1517-1517N can perform a variety of matrix operations to acceleratemachine learning application frameworks, including enabling support foraccelerated general matrix to matrix multiplication (GEMM). The AFUs1512A-1512N can perform additional logic operations not supported by thefloating-point or integer units, including trigonometric operations(e.g., Sine, Cosine, etc.).

FIG. 16A-16B illustrate shared local memory management, according toembodiments. FIG. 16A illustrates a technique in which shared localmemory is cleared between threads for different kernels. FIG. 16Billustrates a technique in which no shared local memory flush occursbetween threads for kernels having a dependency relationship.

As shown in FIG. 16A, in some GPGPU implementations, shared local memory1520 is cleared between thread groups for kernels that execute on anengine block partition 1424. For example, after threads 1601 for azeroth kernel (kernel #0) execute on an engine block partition 1424, theengine block partition will automatically perform an operation 1603 toflush and clear the contents of shared local memory 1520 before threads1602 of the next kernel (kernel #1) are executed. The flush can beperformed to ensure that the threads of kernel #1 begin with sharedlocal memory in a known and initialized state.

As shown in FIG. 16B, embodiments described herein provide a mechanismby which a dependency relationship can be declared between kernels andthread groups for those kernels can be executed without clearing theshared local memory 1520. In one embodiment, a programmer can declarethat multiple kernels (e.g., kernel 0, kernel 1) have a dependencyrelationship. For example, kernel #1 can be declared as depending onoutput generated by kernel #0. The compiler for the kernel can recognizethe dependency declaration and generate instructions to configure theengine block partition 1424 to bypass the flush of the shared localmemory 1520.

During execution on the engine block partition 1424, threads 1611 forkernel #0 can execute, followed by threads 1612 of kernel #1. The engineblock partition will perform an operation 1613 to check if a dependencyrelationship exists between kernel #0 and kernel #1 and will not flushthe shared local memory 1520 if such relationship exists. The specificmanner in which the dependency declarations and dependency checks occurcan vary between embodiments, with example embodiments described below.This technique can be used, for example, to enable successive kernelsthat compute activations for successive layers of a neural network topass activation data within the shared local memory 1520 instead of theglobal memory of the graphics processing device, which can significantlyincrease the processing speed for the neural network.

FIG. 17A-17B illustrate hardware and software operations oninterdependent kernels, according to embodiments described herein. FIG.17A illustrates an exemplary set of kernels executed by host programmingcode. FIG. 17B illustrates execution of the exemplary kernels.

As shown in FIG. 17A, a first call to a zeroth kernel 1701 (kernel #0)can specify a grid size and input parameters (e.g., x, y). The inputparameters can be, for example, a set of matrices on which an operationis to be performed. A second call to a first kernel 1702 (kernel #1) canalso specify a grid size and a set of input parameters. The second callcan also indicate that kernel #1 has a dependency upon kernel #0 (e.g.,dep={kernel #0). A third call to a second kernel 1703 (kernel #2) canspecify a grid size, a set of input parameters, and a dependency uponkernel #0 and kernel #1 (e.g., dep={kernel #0, kernel #1}). The inputparameters can be the same or differ between each kernel.

As shown in FIG. 17B, during execution, the zeroth kernel 1701, firstkernel 1702, and second kernel 1703 can perform operations on theirrespective input parameters and transfer data via the shared localmemory. Kernel #1 can use the output data written by kernel #0 as atleast a portion of the input used to perform its operations.

Kernel #1 can then write at least a portion of its output to the sharedlocal memory, which can be used by kernel #2. The output of kernel #2can be written to device memory and may be subsequently copied to hostmemory. Kernel #0 and kernel #1 may also write at least a portion oftheir output to device memory. The amount of memory that is passedbetween kernels within shared local memory can vary based on the type ofoperations performed by the kernels and the size of the shared localmemory. When the third kernel 1705 does not specify a dependency withkernel #0, kernel #1, and/or kernel #2 the engine block portion can thenperform an operation 1704 to flush and/or clear the shared local memorybefore a third kernel 1705 (kernel #3) is executed.

A grid of threads for a kernel can include multiple thread blocks. Athread block can be mapped to a multiprocessor or engine block partitiondescribed herein (e.g., engine block partition 1424). Multiple threadblocks can be assigned to the processing elements of amultiprocessor/partition. The multiple thread blocks can be executedconcurrently, with the threads of the threads of the different threadblocks executed in an interleaved manner. An engine block partition willschedule additional thread groups as processing resources and sharedlocal memory become available. However, threads of different threadblocks are generally prevented from accessing the same portions ofshared local memory. Some embodiments described herein providetechniques to enable and/or favor concurrent execution of thread groupsfrom interdependent kernels. Interdependent kernels are defined as a setof kernels that include at least some dependency relationship, includingone way or two-way dependencies between kernels. Such kernels caninclude interrelated operations within separate kernels that areperformed serially or concurrently, with explicit synchronization codedinto kernels that are intended for concurrent execution. Suchinterdependent kernels can be scheduled for simultaneous execution andenabled to access the same regions of shared local memory.

FIG. 18 illustrates concurrent execution of interdependent threadgroups, according to embodiments. In one embodiment a set ofinterdependent kernels (kernel #0 1801, kernel #1 1802, kernel #2 1803)having at least one-way dependencies can be scheduled by a threadscheduler 1506 and dispatched by a thread dispatcher 1508 within anengine block partition described herein. The threads can be dispatchedto multiple processing elements 1801A-1801N within the engine blockpartition. The threads of the interdependent kernels can executeconcurrently, while accessing the same portions of the shared localmemory 1820, without the shared local memory being flushed and/orcleared while the interdependent kernels are executing. The processingelements 1801A-1801N can then execute a flush command before executingthreads associated with a kernel that is not dependent upon operationsof other kernels (e.g., kernel #3 1804).

FIG. 19 illustrates a method 1900 of local memory sharing betweenkernels, according to an embodiment. Method 1900 can be performed inpart by a compiler for a shader program to be executed by a GPGPU oranother parallel processor, and in part by an engine block partitionand/or streaming multiprocessor within a GPGPU.

In one embodiment, a shader compiler executing on a host processor canload shader program code for compilation (block 1902). The shadercompiler can detect that the shader program calls multipleinterdependent kernels using the same grid size and/or number of threads(block 1904). The interdependency between the multiple kernels can bedeclared by a programmer of the shader code using techniques describedherein, with the dependency being a one way or multi-way dependencybetween kernels.

Alternatively, the compiler can automatically determine a dependencybetween the kernels based on analysis of the program code of thekernels. The compiler can then mark interdependent kernels as executablewithout clearing shared local memory (block 1906).

When the shader program is executed on the parallel processor device,the shader program can call the set of multiple kernels within theshader program. A scheduler within the parallel processor can schedule athread group for a kernel on a partition of the parallel processordevice (block 1908). The parallel processor can bypass clearing theshared local memory when the kernel is marked as dependent on previouskernel (block 1910).

FIG. 20 illustrates an additional method 2000 of local memory sharingbetween kernels. Method 2000 can be performed on a parallel processor,such as a GPGPU described herein. In one embodiment, method 2000 isperformed on a partition of a parallel processor, such as an engineblock partition and/or streaming multiprocessor within a GPGPU.

Method 2000 includes for a partition on a parallel processor to receivea first kernel and a second kernel for execution (block 2002). Thepartition of the parallel processor can detect that the first kernel andthe second kernel are interdependent, or otherwise have a dependencyrelationship (block 2004). A scheduler within the partition of theparallel processor can detect the interdependence between the kernelsbased on compiler inserted metadata or flags that are generated based onprogrammer inserted dependency data. The scheduler can then schedule afirst thread group for the first kernel and a second thread group forthe second kernel for concurrent execution (block 2006). The partitionof the parallel processor can also enable the first thread group and thesecond thread group to access overlapping regions of shared local memory(block 2008).

Additional Exemplary Graphics Processing System

Details of the embodiments described above can be incorporated withingraphics processing systems and devices described below. The graphicsprocessing system and devices of FIG. 21 through FIG. 33A-33B illustratealternative systems and graphics processing hardware that can implementany and all of the techniques described above.

System Overview

FIG. 21 is a block diagram of a processing system 2100, according to anembodiment. System 2100 may be used in a single processor desktopsystem, a multiprocessor workstation system, or a server system having alarge number of processors 2102 or processor cores 2107. In oneembodiment, the system 2100 is a processing platform incorporated withina system-on-a-chip (SoC) integrated circuit for use in mobile, handheld,or embedded devices such as within Internet-of-things (IoT) devices withwired or wireless connectivity to a local or wide area network.

In one embodiment, system 2100 can include, couple with, or beintegrated within: a server-based gaming platform; a game console,including a game and media console; a mobile gaming console, a handheldgame console, or an online game console. In some embodiments the system2100 is part of a mobile phone, smart phone, tablet computing device ormobile Internet-connected device such as a laptop with low internalstorage capacity. Processing system 2100 can also include, couple with,or be integrated within: a wearable device, such as a smart watchwearable device; smart eyewear or clothing enhanced with augmentedreality (AR) or virtual reality (VR) features to provide visual, audioor tactile outputs to supplement real world visual, audio or tactileexperiences or otherwise provide text, audio, graphics, video,holographic images or video, or tactile feedback; other augmentedreality (AR) device; or other virtual reality (VR) device. In someembodiments, the processing system 2100 includes or is part of atelevision or set top box device.

In some embodiments, system 2100 can include, couple with, or beintegrated within a self-driving vehicle such as a bus, tractor trailer,car, motor or electric power cycle, plane or glider (or any combinationthereof). The self-driving vehicle may use system 2100 to process theenvironment sensed around the vehicle.

In some embodiments, the one or more processors 2102 each include one ormore processor cores 2107 to process instructions which, when executed,perform operations for system or user software. In some embodiments, atleast one of the one or more processor cores 2107 is configured toprocess a specific instruction set 2109. In some embodiments,instruction set 2109 may facilitate Complex Instruction Set Computing(CISC), Reduced Instruction Set Computing (RISC), or computing via aVery Long Instruction Word (VLIW). One or more processor cores 2107 mayprocess a different instruction set 2109, which may include instructionsto facilitate the emulation of other instruction sets. Processor core2107 may also include other processing devices, such as a Digital SignalProcessor (DSP).

In some embodiments, the processor 2102 includes cache memory 2104.Depending on the architecture, the processor 2102 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 2102. In some embodiments, the processor 2102 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 2107 using knowncache coherency techniques. A register file 2106 can be additionallyincluded in processor 2102 and may include different types of registersfor storing different types of data (e.g., integer registers, floatingpoint registers, status registers, and an instruction pointer register).Some registers may be general-purpose registers, while other registersmay be specific to the design of the processor 2102.

In some embodiments, one or more processor(s) 2102 are coupled with oneor more interface bus(es) 2110 to transmit communication signals such asaddress, data, or control signals between processor 2102 and othercomponents in the system 2100. The interface bus 2110, in oneembodiment, can be a processor bus, such as a version of the DirectMedia Interface (DMI) bus. However, processor busses are not limited tothe DMI bus, and may include one or more Peripheral ComponentInterconnect buses (e.g., PCI, PCI Express), memory busses, or othertypes of interface busses. In one embodiment the processor(s) 2102include an integrated memory controller 2116 and a platform controllerhub 2130. The memory controller 2116 facilitates communication between amemory device and other components of the system 2100, while theplatform controller hub (PCH) 2130 provides connections to I/O devicesvia a local I/O bus.

The memory device 2120 can be a dynamic random access memory (DRAM)device, a static random access memory (SRAM) device, flash memorydevice, phase-change memory device, or some other memory device havingsuitable performance to serve as process memory. In one embodiment thememory device 2120 can operate as system memory for the system 2100, tostore data 2122 and instructions 2121 for use when the one or moreprocessors 2102 executes an application or process. Memory controller2116 also couples with an optional external graphics processor 2118,which may communicate with the one or more graphics processors 2108 inprocessors 2102 to perform graphics and media operations. In someembodiments, graphics, media, and or compute operations may be assistedby an accelerator 2112, which is a coprocessor that can be configured toperform a specialized set of graphics, media, or compute operations. Forexample, in one embodiment the accelerator 2112 is a matrixmultiplication accelerator used to optimize machine learning or computeoperations. In one embodiment the accelerator 2112 is a ray-tracingaccelerator that can be used to perform ray-tracing operations inconcert with the graphics processor 2108. In some embodiments a displaydevice 2111 can connect to the processor(s) 2102. The display device2111 can be one or more of an internal display device, as in a mobileelectronic device or a laptop device or an external display deviceattached via a display interface (e.g., DisplayPort, etc.). In oneembodiment the display device 2111 can be a head mounted display (HMD)such as a stereoscopic display device for use in virtual reality (VR)applications or augmented reality (AR) applications.

In some embodiments the platform controller hub 2130 enables peripheralsto connect to memory device 2120 and processor 2102 via a high-speed I/Obus. The I/O peripherals include, but are not limited to, an audiocontroller 2146, a network controller 2134, a firmware interface 2128, awireless transceiver 2126, touch sensors 2125, a data storage device2124 (e.g., non-volatile memory, volatile memory, hard disk drive, flashmemory, NAND, 3D NAND, 3D XPoint, etc.). The data storage device 2124can connect via a storage interface (e.g., SATA) or via a peripheralbus, such as a Peripheral Component Interconnect bus (e.g., PCI, PCIExpress). The touch sensors 2125 can include touch screen sensors,pressure sensors, or fingerprint sensors. The wireless transceiver 2126can be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile networktransceiver such as a 3G, 4G, 5G, or Long Term Evolution (LTE)transceiver. The firmware interface 2128 enables communication withsystem firmware, and can be, for example, a unified extensible firmwareinterface (UEFI). The network controller 2134 can enable a networkconnection to a wired network. In some embodiments, a high-performancenetwork controller (not shown) couples with the interface bus 2110. Theaudio controller 2146, in one embodiment, is a multi-channel highdefinition audio controller. In one embodiment the system 2100 includesan optional legacy I/O controller 2140 for coupling legacy (e.g.,Personal System 2 (PS/2)) devices to the system. The platform controllerhub 2130 can also connect to one or more Universal Serial Bus (USB)controllers 2142 connect input devices, such as keyboard and mouse 2143combinations, a camera 2144, or other USB input devices.

It will be appreciated that the system 2100 shown is exemplary and notlimiting, as other types of data processing systems that are differentlyconfigured may also be used. For example, an instance of the memorycontroller 2116 and platform controller hub 2130 may be integrated intoa discreet external graphics processor, such as the external graphicsprocessor 2118. In one embodiment the platform controller hub 2130and/or memory controller 2116 may be external to the one or moreprocessor(s) 2102. For example, the system 2100 can include an externalmemory controller 2116 and platform controller hub 2130, which may beconfigured as a memory controller hub and peripheral controller hubwithin a system chipset that is in communication with the processor(s)2102.

For example, circuit boards (“sleds”) can be used on which componentssuch as CPUs, memory, and other components are placed are designed forincreased thermal performance. In some examples, processing componentssuch as the processors are located on a top side of a sled while nearmemory, such as DIMMs, are located on a bottom side of the sled. As aresult of the enhanced airflow provided by this design, the componentsmay operate at higher frequencies and power levels than in typicalsystems, thereby increasing performance. Furthermore, the sleds areconfigured to blindly mate with power and data communication cables in arack, thereby enhancing their ability to be quickly removed, upgraded,reinstalled, and/or replaced. Similarly, individual components locatedon the sleds, such as processors, accelerators, memory, and data storagedrives, are configured to be easily upgraded due to their increasedspacing from each other. In the illustrative embodiment, the componentsadditionally include hardware attestation features to prove theirauthenticity.

A data center can utilize a single network architecture (“fabric”) thatsupports multiple other network architectures including Ethernet andOmni-Path. The sleds can be coupled to switches via optical fibers,which provide higher bandwidth and lower latency than typical twistedpair cabling (e.g., Category 5, Category 5e, Category 6, etc.). Due tothe high bandwidth, low latency interconnections and networkarchitecture, the data center may, in use, pool resources, such asmemory, accelerators (e.g., GPUs, graphics accelerators, FPGAs, ASICs,neural network and/or artificial intelligence accelerators, etc.), anddata storage drives that are physically disaggregated, and provide themto compute resources (e.g., processors) on an as needed basis, enablingthe compute resources to access the pooled resources as if they werelocal.

A power supply or source can provide voltage and/or current to system2100 or any component or system described herein. In one example, thepower supply includes an AC to DC (alternating current to directcurrent) adapter to plug into a wall outlet. Such AC power can berenewable energy (e.g., solar power) power source. In one example, powersource includes a DC power source, such as an external AC to DCconverter. In one example, power source or power supply includeswireless charging hardware to charge via proximity to a charging field.In one example, power source can include an internal battery,alternating current supply, motion-based power supply, solar powersupply, or fuel cell source.

FIG. 22 is a block diagram of an embodiment of a processor 2200 havingone or more processor cores 2202A-2202N, an integrated memory controller2214, and an integrated graphics processor 2208. Those elements of FIG.22 having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Processor2200 can include additional cores up to and including additional core2202N represented by the dashed lined boxes. Each of processor cores2202A-2202N includes one or more internal cache units 2204A-2204N. Insome embodiments each processor core also has access to one or moreshared cached units 2206.

The internal cache units 2204A-2204N and shared cache units 2206represent a cache memory hierarchy within the processor 2200. The cachememory hierarchy may include at least one level of instruction and datacache within each processor core and one or more levels of sharedmid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), orother levels of cache, where the highest level of cache before externalmemory is classified as the LLC. In some embodiments, cache coherencylogic maintains coherency between the various cache units 2206 and2204A-2204N.

In some embodiments, processor 2200 may also include a set of one ormore bus controller units 2216 and a system agent core 2210. The one ormore bus controller units 2216 manage a set of peripheral buses, such asone or more PCI or PCI express busses. System agent core 2210 providesmanagement functionality for the various processor components. In someembodiments, system agent core 2210 includes one or more integratedmemory controllers 2214 to manage access to various external memorydevices (not shown).

In some embodiments, one or more of the processor cores 2202A-2202Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 2210 includes components for coordinating andoperating cores 2202A-2202N during multi-threaded processing. Systemagent core 2210 may additionally include a power control unit (PCU),which includes logic and components to regulate the power state ofprocessor cores 2202A-2202N and graphics processor 2208.

In some embodiments, processor 2200 additionally includes graphicsprocessor 2208 to execute graphics processing operations. In someembodiments, the graphics processor 2208 couples with the set of sharedcache units 2206, and the system agent core 2210, including the one ormore integrated memory controllers 2214. In some embodiments, the systemagent core 2210 also includes a display controller 2211 to drivegraphics processor output to one or more coupled displays. In someembodiments, display controller 2211 may also be a separate modulecoupled with the graphics processor via at least one interconnect, ormay be integrated within the graphics processor 2208.

In some embodiments, a ring-based interconnect 2212 is used to couplethe internal components of the processor 2200. However, an alternativeinterconnect unit may be used, such as a point-to-point interconnect, aswitched interconnect, or other techniques, including techniques wellknown in the art. In some embodiments, graphics processor 2208 coupleswith the ring-based interconnect 2212 via an I/O link 2213.

The exemplary I/O link 2213 represents at least one of multiplevarieties of I/O interconnects, including an on package I/O interconnectwhich facilitates communication between various processor components anda high-performance embedded memory module 2218, such as an eDRAM module.In some embodiments, each of the processor cores 2202A-2202N andgraphics processor 2208 can use embedded memory modules 2218 as a sharedLast Level Cache.

In some embodiments, processor cores 2202A-2202N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 2202A-2202N are heterogeneous in terms of instructionset architecture (ISA), where one or more of processor cores 2202A-2202Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment, processor cores 2202A-2202N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. In one embodiment,processor cores 2202A-2202N are heterogeneous in terms of computationalcapability. Additionally, processor 2200 can be implemented on one ormore chips or as an SoC integrated circuit having the illustratedcomponents, in addition to other components.

FIG. 23 is a block diagram of a graphics processor 2300, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores, or other semiconductordevices such as, but not limited to, memory devices or networkinterfaces. In some embodiments, the graphics processor communicates viaa memory mapped I/O interface to registers on the graphics processor andwith commands placed into the processor memory. In some embodiments,graphics processor 2300 includes a memory interface 2314 to accessmemory. Memory interface 2314 can be an interface to local memory, oneor more internal caches, one or more shared external caches, and/or tosystem memory.

In some embodiments, graphics processor 2300 also includes a displaycontroller 2302 to drive display output data to a display device 2318.Display controller 2302 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. The display device 2318 can be an internal orexternal display device. In one embodiment the display device 2318 is ahead mounted display device, such as a virtual reality (VR) displaydevice or an augmented reality (AR) display device. In some embodiments,graphics processor 2300 includes a video codec engine 2306 to encode,decode, or transcode media to, from, or between one or more mediaencoding formats, including, but not limited to Moving Picture ExpertsGroup (MPEG) formats such as MPEG-2, Advanced Video Coding (AVC) formatssuch as H.264/MPEG-4 AVC, H.265/HEVC, Alliance for Open Media (AOMedia)VP8, VP9, as well as the Society of Motion Picture & TelevisionEngineers (SMPTE) 421M/VC-1, and Joint Photographic Experts Group (JPEG)formats such as JPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 2300 includes a block imagetransfer (BLIT) engine 2304 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 2310. In someembodiments, GPE 2310 is a compute engine for performing graphicsoperations, including three-dimensional (3D) graphics operations andmedia operations.

In some embodiments, GPE 2310 includes a 3D pipeline 2312 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 2312 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 2315.While 3D pipeline 2312 can be used to perform media operations, anembodiment of GPE 2310 also includes a media pipeline 2316 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 2316 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 2306. In some embodiments, media pipeline 2316 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 2315. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 2315.

In some embodiments, 3D/Media sub-system 2315 includes logic forexecuting threads spawned by 3D pipeline 2312 and media pipeline 2316.In one embodiment, the pipelines send thread execution requests to3D/Media sub-system 2315, which includes thread dispatch logic forarbitrating and dispatching the various requests to available threadexecution resources. The execution resources include an array ofgraphics execution units to process the 3D and media threads. In someembodiments, 3D/Media sub-system 2315 includes one or more internalcaches for thread instructions and data. In some embodiments, thesubsystem also includes shared memory, including registers andaddressable memory, to share data between threads and to store outputdata.

Graphics Processing Engine

FIG. 24 is a block diagram of a graphics processing engine 2410 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 2410 is a version ofthe GPE 2310 shown in FIG. 23. Elements of FIG. 24 having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. For example, the 3Dpipeline 2312 and media pipeline 2316 of FIG. 23 are illustrated. Themedia pipeline 2316 is optional in some embodiments of the GPE 2410 andmay not be explicitly included within the GPE 2410. For example and inat least one embodiment, a separate media and/or image processor iscoupled to the GPE 2410.

In some embodiments, GPE 2410 couples with or includes a commandstreamer 2403, which provides a command stream to the 3D pipeline 2312and/or media pipelines 2316. In some embodiments, command streamer 2403is coupled with memory, which can be system memory, or one or more ofinternal cache memory and shared cache memory. In some embodiments,command streamer 2403 receives commands from the memory and sends thecommands to 3D pipeline 2312 and/or media pipeline 2316. The commandsare directives fetched from a ring buffer, which stores commands for the3D pipeline 2312 and media pipeline 2316. In one embodiment, the ringbuffer can additionally include batch command buffers storing batches ofmultiple commands. The commands for the 3D pipeline 2312 can alsoinclude references to data stored in memory, such as but not limited tovertex and geometry data for the 3D pipeline 2312 and/or image data andmemory objects for the media pipeline 2316. The 3D pipeline 2312 andmedia pipeline 2316 process the commands and data by performingoperations via logic within the respective pipelines or by dispatchingone or more execution threads to a graphics core array 2414. In oneembodiment the graphics core array 2414 include one or more blocks ofgraphics cores (e.g., graphics core(s) 2415A, graphics core(s) 2415B),each block including one or more graphics cores. Each graphics coreincludes a set of graphics execution resources that includesgeneral-purpose and graphics specific execution logic to performgraphics and compute operations, as well as fixed function textureprocessing and/or machine learning and artificial intelligenceacceleration logic.

In various embodiments the 3D pipeline 2312 can include fixed functionand programmable logic to process one or more shader programs, such asvertex shaders, geometry shaders, pixel shaders, fragment shaders,compute shaders, or other shader programs, by processing theinstructions and dispatching execution threads to the graphics corearray 2414. The graphics core array 2414 provides a unified block ofexecution resources for use in processing these shader programs.Multi-purpose execution logic (e.g., execution units) within thegraphics core(s) 2415A-2414B of the graphics core array 2414 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments, the graphics core array 2414 includes executionlogic to perform media functions, such as video and/or image processing.In one embodiment, the execution units include general-purpose logicthat is programmable to perform parallel general-purpose computationaloperations, in addition to graphics processing operations. Thegeneral-purpose logic can perform processing operations in parallel orin conjunction with general-purpose logic within the processor core(s)2107 of FIG. 21 or core 2202A-2202N as in FIG. 22.

Output data generated by threads executing on the graphics core array2414 can output data to memory in a unified return buffer (URB) 2418.The URB 2418 can store data for multiple threads. In some embodimentsthe URB 2418 may be used to send data between different threadsexecuting on the graphics core array 2414. In some embodiments the URB2418 may additionally be used for synchronization between threads on thegraphics core array and fixed function logic within the shared functionlogic 2420.

In some embodiments, graphics core array 2414 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 2410. In one embodiment the execution resourcesare dynamically scalable, such that execution resources may be enabledor disabled as needed.

The graphics core array 2414 couples with shared function logic 2420that includes multiple resources that are shared between the graphicscores in the graphics core array. The shared functions within the sharedfunction logic 2420 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 2414. In variousembodiments, shared function logic 2420 includes but is not limited tosampler 2421, math 2422, and inter-thread communication (ITC) 2423logic.

Additionally, some embodiments implement one or more cache(s) 2425within the shared function logic 2420.

A shared function is implemented at least in a case where the demand fora given specialized function is insufficient for inclusion within thegraphics core array 2414. Instead a single instantiation of thatspecialized function is implemented as a stand-alone entity in theshared function logic 2420 and shared among the execution resourceswithin the graphics core array 2414. The precise set of functions thatare shared between the graphics core array 2414 and included within thegraphics core array 2414 varies across embodiments. In some embodiments,specific shared functions within the shared function logic 2420 that areused extensively by the graphics core array 2414 may be included withinshared function logic 2416 within the graphics core array 2414. Invarious embodiments, the shared function logic 2416 within the graphicscore array 2414 can include some or all logic within the shared functionlogic 2420. In one embodiment, all logic elements within the sharedfunction logic 2420 may be duplicated within the shared function logic2416 of the graphics core array 2414. In one embodiment the sharedfunction logic 2420 is excluded in favor of the shared function logic2416 within the graphics core array 2414.

FIG. 25 is a block diagram of hardware logic of a graphics processorcore 2500, according to some embodiments described herein. Elements ofFIG. 25 having the same reference numbers (or names) as the elements ofany other figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Theillustrated graphics processor core 2500, in some embodiments, isincluded within the graphics core array 2414 of FIG. 24. The graphicsprocessor core 2500, sometimes referred to as a core slice, can be oneor multiple graphics cores within a modular graphics processor. Thegraphics processor core 2500 is exemplary of one graphics core slice,and a graphics processor as described herein may include multiplegraphics core slices based on target power and performance envelopes.Each graphics processor core 2500 can include a fixed function block2530 coupled with multiple sub-cores 2501A-2501F, also referred to assub-slices, that include modular blocks of general-purpose and fixedfunction logic.

In some embodiments, the fixed function block 2530 includes ageometry/fixed function pipeline 2536 that can be shared by allsub-cores in the graphics processor core 2500, for example, in lowerperformance and/or lower power graphics processor implementations. Invarious embodiments, the geometry/fixed function pipeline 2536 includesa 3D fixed function pipeline (e.g., 3D pipeline 2312 as in FIG. 23 andFIG. 24) a video front-end unit, a thread spawner and thread dispatcher,and a unified return buffer manager, which manages unified returnbuffers, such as the unified return buffer 2418 of FIG. 24.

In one embodiment the fixed function block 2530 also includes a graphicsSoC interface 2537, a graphics microcontroller 2538, and a mediapipeline 2539. The graphics SoC interface 2537 provides an interfacebetween the graphics processor core 2500 and other processor coreswithin a system on a chip integrated circuit. The graphicsmicrocontroller 2538 is a programmable sub-processor that isconfigurable to manage various functions of the graphics processor core2500, including thread dispatch, scheduling, and pre-emption. The mediapipeline 2539 (e.g., media pipeline 2316 of FIG. 23 and FIG. 24)includes logic to facilitate the decoding, encoding, pre-processing,and/or post-processing of multimedia data, including image and videodata. The media pipeline 2539 implement media operations via requests tocompute or sampling logic within the sub-cores 2501-2501F.

In one embodiment the SoC interface 2537 enables the graphics processorcore 2500 to communicate with general-purpose application processorcores (e.g., CPUs) and/or other components within an SoC, includingmemory hierarchy elements such as a shared last level cache memory, thesystem RAM, and/or embedded on-chip or on-package DRAM. The SoCinterface 2537 can also enable communication with fixed function deviceswithin the SoC, such as camera imaging pipelines, and enables the use ofand/or implements global memory atomics that may be shared between thegraphics processor core 2500 and CPUs within the SoC. The SoC interface2537 can also implement power management controls for the graphicsprocessor core 2500 and enable an interface between a clock domain ofthe graphic core 2500 and other clock domains within the SoC. In oneembodiment the SoC interface 2537 enables receipt of command buffersfrom a command streamer and global thread dispatcher that are configuredto provide commands and instructions to each of one or more graphicscores within a graphics processor. The commands and instructions can bedispatched to the media pipeline 2539, when media operations are to beperformed, or a geometry and fixed function pipeline (e.g., geometry andfixed function pipeline 2536, geometry and fixed function pipeline 2514)when graphics processing operations are to be performed.

The graphics microcontroller 2538 can be configured to perform variousscheduling and management tasks for the graphics processor core 2500. Inone embodiment the graphics microcontroller 2538 can perform graphicsand/or compute workload scheduling on the various graphics parallelengines within execution unit (EU) arrays 2502A-2502F, 2504A-2504Fwithin the sub-cores 2501A-2501F. In this scheduling model, hostsoftware executing on a CPU core of an SoC including the graphicsprocessor core 2500 can submit workloads one of multiple graphicprocessor doorbells, which invokes a scheduling operation on theappropriate graphics engine. Scheduling operations include determiningwhich workload to run next, submitting a workload to a command streamer,pre-empting existing workloads running on an engine, monitoring progressof a workload, and notifying host software when a workload is complete.In one embodiment the graphics microcontroller 2538 can also facilitatelow-power or idle states for the graphics processor core 2500, providingthe graphics processor core 2500 with the ability to save and restoreregisters within the graphics processor core 2500 across low-power statetransitions independently from the operating system and/or graphicsdriver software on the system.

The graphics processor core 2500 may have greater than or fewer than theillustrated sub-cores 2501A-2501F, up to N modular sub-cores. For eachset of N sub-cores, the graphics processor core 2500 can also includeshared function logic 2510, shared and/or cache memory 2512, ageometry/fixed function pipeline 2514, as well as additional fixedfunction logic 2516 to accelerate various graphics and computeprocessing operations. The shared function logic 2510 can include logicunits associated with the shared function logic 2420 of FIG. 24 (e.g.,sampler, math, and/or inter-thread communication logic) that can beshared by each N sub-cores within the graphics processor core 2500. Theshared and/or cache memory 2512 can be a last-level cache for the set ofN sub-cores 2501A-2501F within the graphics processor core 2500, and canalso serve as shared memory that is accessible by multiple sub-cores.The geometry/fixed function pipeline 2514 can be included instead of thegeometry/fixed function pipeline 2536 within the fixed function block2530 and can include the same or similar logic units.

In one embodiment the graphics processor core 2500 includes additionalfixed function logic 2516 that can include various fixed functionacceleration logic for use by the graphics processor core 2500. In oneembodiment the additional fixed function logic 2516 includes anadditional geometry pipeline for use in position only shading. Inposition-only shading, two geometry pipelines exist, the full geometrypipeline within the geometry/fixed function pipeline 2536, and a cullpipeline, which is an additional geometry pipeline which may be includedwithin the additional fixed function logic 2516. In one embodiment thecull pipeline is a trimmed down version of the full geometry pipeline.The full pipeline and the cull pipeline can execute different instancesof the same application, each instance having a separate context.Position only shading can hide long cull runs of discarded triangles,enabling shading to be completed earlier in some instances. For exampleand in one embodiment the cull pipeline logic within the additionalfixed function logic 2516 can execute position shaders in parallel withthe main application and generally generates critical results fasterthan the full pipeline, as the cull pipeline fetches and shades only theposition attribute of the vertices, without performing rasterization andrendering of the pixels to the frame buffer. The cull pipeline can usethe generated critical results to compute visibility information for allthe triangles without regard to whether those triangles are culled. Thefull pipeline (which in this instance may be referred to as a replaypipeline) can consume the visibility information to skip the culledtriangles to shade only the visible triangles that are finally passed tothe rasterization phase.

In one embodiment the additional fixed function logic 2516 can alsoinclude machine-learning acceleration logic, such as fixed functionmatrix multiplication logic, for implementations including optimizationsfor machine learning training or inferencing.

Within each graphics sub-core 2501A-2501F includes a set of executionresources that may be used to perform graphics, media, and computeoperations in response to requests by graphics pipeline, media pipeline,or shader programs. The graphics sub-cores 2501A-2501F include multipleEU arrays 2502A-2502F, 2504A-2504F, thread dispatch and inter-threadcommunication (TD/IC) logic 2503A-2503F, a 3D (e.g., texture) sampler2505A-2505F, a media sampler 2506A-2506F, a shader processor2507A-2507F, and shared local memory (SLM) 2508A-2508F. The EU arrays2502A-2502F, 2504A-2504F each include multiple execution units, whichare general-purpose graphics processing units capable of performingfloating-point and integer/fixed-point logic operations in service of agraphics, media, or compute operation, including graphics, media, orcompute shader programs. The TD/IC logic 2503A-2503F performs localthread dispatch and thread control operations for the execution unitswithin a sub-core and facilitate communication between threads executingon the execution units of the sub-core. The 3D sampler 2505A-2505F canread texture or other 3D graphics related data into memory. The 3Dsampler can read texture data differently based on a configured samplestate and the texture format associated with a given texture. The mediasampler 2506A-2506F can perform similar read operations based on thetype and format associated with media data. In one embodiment, eachgraphics sub-core 2501A-2501F can alternately include a unified 3D andmedia sampler. Threads executing on the execution units within each ofthe sub-cores 2501A-2501F can make use of shared local memory2508A-2508F within each sub-core, to enable threads executing within athread group to execute using a common pool of on-chip memory.

Execution Units

FIG. 26A-26B illustrate thread execution logic 2600 including an arrayof processing elements employed in a graphics processor core accordingto embodiments described herein. Elements of FIG. 26A-26B having thesame reference numbers (or names) as the elements of any other figureherein can operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. FIG. 26A illustrates anoverview of thread execution logic 2600, which can include a variant ofthe hardware logic illustrated with each sub-core 2501A-2501F of FIG.25. FIG. 26B illustrates exemplary internal details of an executionunit.

As illustrated in FIG. 26A, in some embodiments thread execution logic2600 includes a shader processor 2602, a thread dispatcher 2604,instruction cache 2606, a scalable execution unit array including aplurality of execution units 2608A-2608N, a sampler 2610, a data cache2612, and a data port 2614. In one embodiment the scalable executionunit array can dynamically scale by enabling or disabling one or moreexecution units (e.g., any of execution unit 2608A, 2608B, 2608C, 2608D,through 2608N-1 and 2608N) based on the computational requirements of aworkload. In one embodiment the included components are interconnectedvia an interconnect fabric that links to each of the components. In someembodiments, thread execution logic 2600 includes one or moreconnections to memory, such as system memory or cache memory, throughone or more of instruction cache 2606, data port 2614, sampler 2610, andexecution units 2608A-2608N. In some embodiments, each execution unit(e.g. 2608A) is a stand-alone programmable general-purpose computationalunit that is capable of executing multiple simultaneous hardware threadswhile processing multiple data elements in parallel for each thread. Invarious embodiments, the array of execution units 2608A-2608N isscalable to include any number individual execution units.

In some embodiments, the execution units 2608A-2608N are primarily usedto execute shader programs. A shader processor 2602 can process thevarious shader programs and dispatch execution threads associated withthe shader programs via a thread dispatcher 2604. In one embodiment thethread dispatcher includes logic to arbitrate thread initiation requestsfrom the graphics and media pipelines and instantiate the requestedthreads on one or more execution unit in the execution units2608A-2608N. For example, a geometry pipeline can dispatch vertex,tessellation, or geometry shaders to the thread execution logic forprocessing. In some embodiments, thread dispatcher 2604 can also processruntime thread spawning requests from the executing shader programs.

In some embodiments, the execution units 2608A-2608N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. The execution units support vertex and geometry processing(e.g., vertex programs, geometry programs, vertex shaders), pixelprocessing (e.g., pixel shaders, fragment shaders) and general-purposeprocessing (e.g., compute and media shaders). Each of the executionunits 2608A-2608N is capable of multi-issue single instruction multipledata (SIMD) execution and multi-threaded operation enables an efficientexecution environment in the face of higher latency memory accesses.Each hardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state.Execution is multi-issue per clock to pipelines capable of integer,single and double precision floating point operations, SIMD branchcapability, logical operations, transcendental operations, and othermiscellaneous operations. While waiting for data from memory or one ofthe shared functions, dependency logic within the execution units2608A-2608N causes a waiting thread to sleep until the requested datahas been returned. While the waiting thread is sleeping, hardwareresources may be devoted to processing other threads. For example,during a delay associated with a vertex shader operation, an executionunit can perform operations for a pixel shader, fragment shader, oranother type of shader program, including a different vertex shader.Various embodiments can apply to use execution by use of SingleInstruction Multiple Thread (SIMT) as an alternate to use of SIMD or inaddition to use of SIMD. Reference to a SIMD core or operation can applyalso to SIMT or apply to SIMD in combination with SIMT.

Each execution unit in execution units 2608A-2608N operates on arrays ofdata elements. The number of data elements is the “execution size,” orthe number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical Arithmetic Logic Units (ALUs) or FloatingPoint Units (FPUs) for a particular graphics processor. In someembodiments, execution units 2608A-2608N support integer andfloating-point data types.

The execution unit instruction set includes SIMD instructions. Thevarious data elements can be stored as a packed data type in a registerand the execution unit will process the various elements based on thedata size of the elements. For example, when operating on a 256-bit widevector, the 256 bits of the vector are stored in a register and theexecution unit operates on the vector as four separate 64-bit packeddata elements (Quad-Word (QW) size data elements), eight separate 32-bitpacked data elements (Double Word (DW) size data elements), sixteenseparate 16-bit packed data elements (Word (W) size data elements), orthirty-two separate 8-bit data elements (byte (B) size data elements).However, different vector widths and register sizes are possible.

In one embodiment one or more execution units can be combined into afused execution unit 2609A-2609N having thread control logic(2607A-2607N) that is common to the fused EUs. Multiple EUs can be fusedinto an EU group. Each EU in the fused EU group can be configured toexecute a separate SIMD hardware thread. The number of EUs in a fused EUgroup can vary according to embodiments. Additionally, various SIMDwidths can be performed per-EU, including but not limited to SIMD8,SIMD16, and SIMD32. Each fused graphics execution unit 2609A-2609Nincludes at least two execution units. For example, fused execution unit2609A includes a first EU 2608A, second EU 2608B, and thread controllogic 2607A that is common to the first EU 2608A and the second EU2608B. The thread control logic 2607A controls threads executed on thefused graphics execution unit 2609A, allowing each EU within the fusedexecution units 2609A-2609N to execute using a common instructionpointer register.

One or more internal instruction caches (e.g., 2606) are included in thethread execution logic 2600 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,2612) are included to cache thread data during thread execution. In someembodiments, a sampler 2610 is included to provide texture sampling for3D operations and media sampling for media operations. In someembodiments, sampler 2610 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 2600 via thread spawningand dispatch logic. Once a group of geometric objects has been processedand rasterized into pixel data, pixel processor logic (e.g., pixelshader logic, fragment shader logic, etc.) within the shader processor2602 is invoked to further compute output information and cause resultsto be written to output surfaces (e.g., color buffers, depth buffers,stencil buffers, etc.). In some embodiments, a pixel shader or fragmentshader calculates the values of the various vertex attributes that areto be interpolated across the rasterized object. In some embodiments,pixel processor logic within the shader processor 2602 then executes anapplication programming interface (API)-supplied pixel or fragmentshader program. To execute the shader program, the shader processor 2602dispatches threads to an execution unit (e.g., 2608A) via threaddispatcher 2604. In some embodiments, shader processor 2602 uses texturesampling logic in the sampler 2610 to access texture data in texturemaps stored in memory. Arithmetic operations on the texture data and theinput geometry data compute pixel color data for each geometricfragment, or discards one or more pixels from further processing.

In some embodiments, the data port 2614 provides a memory accessmechanism for the thread execution logic 2600 to output processed datato memory for further processing on a graphics processor outputpipeline. In some embodiments, the data port 2614 includes or couples toone or more cache memories (e.g., data cache 2612) to cache data formemory access via the data port.

As illustrated in FIG. 26B, a graphics execution unit 2608 can includean instruction fetch unit 2637, a general register file array (GRF)2624, an architectural register file array (ARF) 2626, a thread arbiter2622, a send unit 2630, a branch unit 2632, a set of SIMD floating pointunits (FPUs) 2634, and in one embodiment a set of dedicated integer SIMDALUs 2635. The GRF 2624 and ARF 2626 includes the set of generalregister files and architecture register files associated with eachsimultaneous hardware thread that may be active in the graphicsexecution unit 2608. In one embodiment, per thread architectural stateis maintained in the ARF 2626, while data used during thread executionis stored in the GRF 2624. The execution state of each thread, includingthe instruction pointers for each thread, can be held in thread-specificregisters in the ARF 2626.

In one embodiment the graphics execution unit 2608 has an architecturethat is a combination of Simultaneous Multi-Threading (SMT) andfine-grained Interleaved Multi-Threading (IMT). The architecture has amodular configuration that can be fine-tuned at design time based on atarget number of simultaneous threads and number of registers perexecution unit, where execution unit resources are divided across logicused to execute multiple simultaneous threads.

In one embodiment, the graphics execution unit 2608 can co-issuemultiple instructions, which may each be different instructions. Thethread arbiter 2622 of the graphics execution unit 2608 can dispatch theinstructions to one of the send unit 2630, branch unit 2632, or SIMDFPU(s) 2634 for execution. Each execution thread can access 128general-purpose registers within the GRF 2624, where each register canstore 32 bytes, accessible as an 8-element vector of 32-bit dataelements. In one embodiment, each execution unit thread has access to 4Kbytes within the GRF 2624, although embodiments are not so limited, andgreater or fewer register resources may be provided in otherembodiments. In one embodiment up to seven threads can executesimultaneously, although the number of threads per execution unit canalso vary according to embodiments. In an embodiment in which seventhreads may access 4 Kbytes, the GRF 2624 can store a total of 28Kbytes. Flexible addressing modes can permit registers to be addressedtogether to build effectively wider registers or to represent stridedrectangular block data structures.

In one embodiment, memory operations, sampler operations, and otherlonger-latency system communications are dispatched via “send”instructions that are executed by the message passing send unit 2630. Inone embodiment, branch instructions are dispatched to a dedicated branchunit 2632 to facilitate SIMD divergence and eventual convergence.

In one embodiment the graphics execution unit 2608 includes one or moreSIMD floating point units (FPU(s)) 2634 to perform floating-pointoperations. In one embodiment, the FPU(s) 2634 also support integercomputation. In one embodiment the FPU(s) 2634 can SIMD execute up to Mnumber of 32-bit floating-point (or integer) operations, or SIMD executeup to 2M 16-bit integer or 16-bit floating-point operations. In oneembodiment, at least one of the FPU(s) provides extended math capabilityto support high-throughput transcendental math functions and doubleprecision 64-bit floating-point. In some embodiments, a set of 8-bitinteger SIMD ALUs 2635 are also present and may be specificallyoptimized to perform operations associated with machine learningcomputations.

In one embodiment, arrays of multiple instances of the graphicsexecution unit 2608 can be instantiated in a graphics sub-core grouping(e.g., a sub-slice). For scalability, product architects can choose theexact number of execution units per sub-core grouping. In one embodimentthe execution unit 2608 can execute instructions across a plurality ofexecution channels. In a further embodiment, each thread executed on thegraphics execution unit 2608 is executed on a different channel.

FIG. 27 is a block diagram illustrating a graphics processor instructionformats 2700 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 2700 described and illustrated aremacro-instructions, in that they are instructions supplied to theexecution unit, as opposed to micro-operations resulting frominstruction decode once the instruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 2710. A 64-bitcompacted instruction format 2730 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 2710 provides access toall instruction options, while some options and operations arerestricted in the 64-bit format 2730. The native instructions availablein the 64-bit format 2730 vary by embodiment. In some embodiments, theinstruction is compacted in part using a set of index values in an indexfield 2713. The execution unit hardware references a set of compactiontables based on the index values and uses the compaction table outputsto reconstruct a native instruction in the 128-bit instruction format2710. Other sizes and formats of instruction can be used.

For each format, instruction opcode 2712 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 2714 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). Forinstructions in the 128-bit instruction format 2710 an exec-size field2716 limits the number of data channels that will be executed inparallel. In some embodiments, exec-size field 2716 is not available foruse in the 64-bit compact instruction format 2730.

Some execution unit instructions have up to three operands including twosource operands, src0 2720, src1 2722, and one destination 2718. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 2724), where the instructionopcode 2712 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 2710 includes anaccess/address mode field 2726 specifying, for example, whether directregister addressing mode or indirect register addressing mode is used.When direct register addressing mode is used, the register address ofone or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 2710 includes anaccess/address mode field 2726, which specifies an address mode and/oran access mode for the instruction. In one embodiment the access mode isused to define a data access alignment for the instruction. Someembodiments support access modes including a 16-byte aligned access modeand a 1-byte aligned access mode, where the byte alignment of the accessmode determines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction may use 16-byte-aligned addressing for all sourceand destination operands.

In one embodiment, the address mode portion of the access/address modefield 2726 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction directly provide the register address of one or moreoperands. When indirect register addressing mode is used, the registeraddress of one or more operands may be computed based on an addressregister value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 2712bit-fields to simplify Opcode decode 2740. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 2742 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 2742 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 2744 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 2746 includesa mix of instructions, including synchronization instructions (e.g.,wait, send) in the form of 0011xxxxb (e.g., 0x30). A parallel mathinstruction group 2748 includes component-wise arithmetic instructions(e.g., add, multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). Theparallel math group 2748 performs the arithmetic operations in parallelacross data channels. The vector math group 2750 includes arithmeticinstructions (e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). Thevector math group performs arithmetic such as dot product calculationson vector operands.

Graphics Pipeline

FIG. 28 is a block diagram of another embodiment of a graphics processor2800. Elements of FIG. 28 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 2800 includes a geometrypipeline 2820, a media pipeline 2830, a display engine 2840, threadexecution logic 2850, and a render output pipeline 2870. In someembodiments, graphics processor 2800 is a graphics processor within amulti-core processing system that includes one or more general-purposeprocessing cores. The graphics processor is controlled by registerwrites to one or more control registers (not shown) or via commandsissued to graphics processor 2800 via a ring interconnect 2802. In someembodiments, ring interconnect 2802 couples graphics processor 2800 toother processing components, such as other graphics processors orgeneral-purpose processors. Commands from ring interconnect 2802 areinterpreted by a command streamer 2803, which supplies instructions toindividual components of the geometry pipeline 2820 or the mediapipeline 2830.

In some embodiments, command streamer 2803 directs the operation of avertex fetcher 2805 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 2803. In someembodiments, vertex fetcher 2805 provides vertex data to a vertex shader2807, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 2805 andvertex shader 2807 execute vertex-processing instructions by dispatchingexecution threads to execution units 2852A-2852B via a thread dispatcher2831.

In some embodiments, execution units 2852A-2852B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 2852A-2852B have anattached L1 cache 2851 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, geometry pipeline 2820 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 2811 configures thetessellation operations. A programmable domain shader 2817 providesback-end evaluation of tessellation output. A tessellator 2813 operatesat the direction of hull shader 2811 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to geometry pipeline 2820. Insome embodiments, if tessellation is not used, tessellation components(e.g., hull shader 2811, tessellator 2813, and domain shader 2817) canbe bypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 2819 via one or more threads dispatched to executionunits 2852A-2852B, or can proceed directly to the clipper 2829. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader2819 receives input from the vertex shader 2807. In some embodiments,geometry shader 2819 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 2829 processes vertex data. The clipper2829 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 2873 in the render output pipeline2870 dispatches pixel shaders to convert the geometric objects into perpixel representations. In some embodiments, pixel shader logic isincluded in thread execution logic 2850. In some embodiments, anapplication can bypass the rasterizer and depth test component 2873 andaccess un-rasterized vertex data via a stream out unit 2823.

The graphics processor 2800 has an interconnect bus, interconnectfabric, or some other interconnect mechanism that allows data andmessage passing amongst the major components of the processor. In someembodiments, execution units 2852A-2852B and associated logic units(e.g., L1 cache 2851, sampler 2854, texture cache 2858, etc.)interconnect via a data port 2856 to perform memory access andcommunicate with render output pipeline components of the processor. Insome embodiments, sampler 2854, L1 cache 2851, texture cache 2858, andexecution units 2852A-2852B each have separate memory access paths. Inone embodiment the texture cache 2858 can also be configured as asampler cache.

In some embodiments, render output pipeline 2870 contains a rasterizerand depth test component 2873 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache2878 and depth cache 2879 are also available in some embodiments. Apixel operations component 2877 performs pixel-based operations on thedata, though in some instances, pixel operations associated with 2Doperations (e.g. bit block image transfers with blending) are performedby the 2D engine 2841, or substituted at display time by the displaycontroller 2843 using overlay display planes. In some embodiments, ashared L3 cache 2875 is available to all graphics components, allowingthe sharing of data without the use of main system memory.

In some embodiments, graphics processor media pipeline 2830 includes amedia engine 2837 and a video front-end 2834. In some embodiments, videofront-end 2834 receives pipeline commands from the command streamer2803. In some embodiments, media pipeline 2830 includes a separatecommand streamer. In some embodiments, video front-end 2834 processesmedia commands before sending the command to the media engine 2837. Insome embodiments, media engine 2837 includes thread spawningfunctionality to spawn threads for dispatch to thread execution logic2850 via thread dispatcher 2831.

In some embodiments, graphics processor 2800 includes a display engine2840. In some embodiments, display engine 2840 is external to processor2800 and couples with the graphics processor via the ring interconnect2802, or some other interconnect bus or fabric. In some embodiments,display engine 2840 includes a 2D engine 2841 and a display controller2843. In some embodiments, display engine 2840 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 2843 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, the geometry pipeline 2820 and media pipeline 2830are configurable to perform operations based on multiple graphics andmedia programming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL), Open Computing Language (OpenCL),and/or Vulkan graphics and compute API, all from the Khronos Group. Insome embodiments, support may also be provided for the Direct3D libraryfrom the Microsoft Corporation. In some embodiments, a combination ofthese libraries may be supported. Support may also be provided for theOpen Source Computer Vision Library (OpenCV). A future API with acompatible 3D pipeline would also be supported if a mapping can be madefrom the pipeline of the future API to the pipeline of the graphicsprocessor.

Graphics Pipeline Programming

FIG. 29A is a block diagram illustrating a graphics processor commandformat 2900 according to some embodiments. FIG. 29B is a block diagramillustrating a graphics processor command sequence 2910 according to anembodiment. The solid lined boxes in FIG. 29A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 2900 of FIG. 29A includes fields to identify a client2902, a command operation code (opcode) 2904, and data 2906 for thecommand. A sub-opcode 2905 and a command size 2908 are also included insome commands.

In some embodiments, client 2902 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 2904 and, if present, sub-opcode 2905 to determine theoperation to perform. The client unit performs the command usinginformation in data field 2906. For some commands an explicit commandsize 2908 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word. Othercommand formats can be used.

The flow diagram in FIG. 29B illustrates an exemplary graphics processorcommand sequence 2910. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 2910 maybegin with a pipeline flush command 2912 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 2922 and the media pipeline 2924 donot operate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 2912 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 2913 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 2913is required only once within an execution context before issuingpipeline commands unless the context is to issue commands for bothpipelines. In some embodiments, a pipeline flush command 2912 isrequired immediately before a pipeline switch via the pipeline selectcommand 2913.

In some embodiments, a pipeline control command 2914 configures agraphics pipeline for operation and is used to program the 3D pipeline2922 and the media pipeline 2924. In some embodiments, pipeline controlcommand 2914 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 2914 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, commands to configure the return buffer state 2916are used to configure a set of return buffers for the respectivepipelines to write data. Some pipeline operations require theallocation, selection, or configuration of one or more return buffersinto which the operations write intermediate data during processing. Insome embodiments, the graphics processor also uses one or more returnbuffers to store output data and to perform cross thread communication.In some embodiments, the return buffer state 2916 includes selecting thesize and number of return buffers to use for a set of pipelineoperations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 2920,the command sequence is tailored to the 3D pipeline 2922 beginning withthe 3D pipeline state 2930 or the media pipeline 2924 beginning at themedia pipeline state 2940.

The commands to configure the 3D pipeline state 2930 include 3D statesetting commands for vertex buffer state, vertex element state, constantcolor state, depth buffer state, and other state variables that are tobe configured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based on the particular3D API in use. In some embodiments, 3D pipeline state 2930 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 2932 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 2932 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 2932command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 2932 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 2922 dispatches shader execution threads tographics processor execution units.

In some embodiments, 3D pipeline 2922 is triggered via an execute 2934command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment, commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 2910follows the media pipeline 2924 path when performing media operations.In general, the specific use and manner of programming for the mediapipeline 2924 depends on the media or compute operations to beperformed. Specific media decode operations may be offloaded to themedia pipeline during media decode. In some embodiments, the mediapipeline can also be bypassed and media decode can be performed in wholeor in part using resources provided by one or more general-purposeprocessing cores. In one embodiment, the media pipeline also includeselements for general-purpose graphics processor unit (GPGPU) operations,where the graphics processor is used to perform SIMD vector operationsusing computational shader programs that are not explicitly related tothe rendering of graphics primitives.

In some embodiments, media pipeline 2924 is configured in a similarmanner as the 3D pipeline 2922. A set of commands to configure the mediapipeline state 2940 are dispatched or placed into a command queue beforethe media object commands 2942. In some embodiments, commands for themedia pipeline state 2940 include data to configure the media pipelineelements that will be used to process the media objects. This includesdata to configure the video decode and video encode logic within themedia pipeline, such as encode or decode format. In some embodiments,commands for the media pipeline state 2940 also support the use of oneor more pointers to “indirect” state elements that contain a batch ofstate settings.

In some embodiments, media object commands 2942 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 2942. Once the pipeline state is configured andmedia object commands 2942 are queued, the media pipeline 2924 istriggered via an execute command 2944 or an equivalent execute event(e.g., register write). Output from media pipeline 2924 may then be postprocessed by operations provided by the 3D pipeline 2922 or the mediapipeline 2924. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 30 illustrates an exemplary graphics software architecture for adata processing system 3000 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application3010, an operating system 3020, and at least one processor 3030. In someembodiments, processor 3030 includes a graphics processor 3032 and oneor more general-purpose processor core(s) 3034. The graphics application3010 and operating system 3020 each execute in the system memory 3050 ofthe data processing system.

In some embodiments, 3D graphics application 3010 contains one or moreshader programs including shader instructions 3012. The shader languageinstructions may be in a high-level shader language, such as theHigh-Level Shader Language (HLSL) of Direct3D, the OpenGL ShaderLanguage (GLSL), and so forth. The application also includes executableinstructions 3014 in a machine language suitable for execution by thegeneral-purpose processor core 3034. The application also includesgraphics objects 3016 defined by vertex data.

In some embodiments, operating system 3020 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 3020 can support agraphics API 3022 such as the Direct3D API, the OpenGL API, or theVulkan API. When the Direct3D API is in use, the operating system 3020uses a front-end shader compiler 3024 to compile any shader instructions3012 in HLSL into a lower-level shader language. The compilation may bea just-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 3010. In some embodiments, the shader instructions 3012 areprovided in an intermediate form, such as a version of the StandardPortable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 3026 contains a back-endshader compiler 3027 to convert the shader instructions 3012 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 3012 in the GLSL high-level language are passed to a usermode graphics driver 3026 for compilation. In some embodiments, usermode graphics driver 3026 uses operating system kernel mode functions3028 to communicate with a kernel mode graphics driver 3029. In someembodiments, kernel mode graphics driver 3029 communicates with graphicsprocessor 3032 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 31A is a block diagram illustrating an IP core development system3100 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system3100 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility3130 can generate a software simulation 3110 of an IP core design in ahigh-level programming language (e.g., C/C++). The software simulation3110 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 3112. The simulation model 3112 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 3115 can then be created or synthesized from thesimulation model 3112. The RTL design 3115 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 3115, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 3115 or equivalent may be further synthesized by thedesign facility into a hardware model 3120, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3rdparty fabrication facility 3165 using non-volatile memory 3140 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 3150 or wireless connection 3160. Thefabrication facility 3165 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

FIG. 31B illustrates a cross-section side view of an integrated circuitpackage assembly 3170, according to some embodiments described herein.The integrated circuit package assembly 3170 illustrates animplementation of one or more processor or accelerator devices asdescribed herein. The package assembly 3170 includes multiple units ofhardware logic 3172, 3174 connected to a substrate 3180. The logic 3172,3174 may be implemented at least partly in configurable logic orfixed-functionality logic hardware, and can include one or more portionsof any of the processor core(s), graphics processor(s), or otheraccelerator devices described herein. Each unit of logic 3172, 3174 canbe implemented within a semiconductor die and coupled with the substrate3180 via an interconnect structure 3173. The interconnect structure 3173may be configured to route electrical signals between the logic 3172,3174 and the substrate 3180, and can include interconnects such as, butnot limited to bumps or pillars. In some embodiments, the interconnectstructure 3173 may be configured to route electrical signals such as,for example, input/output (I/O) signals and/or power or ground signalsassociated with the operation of the logic 3172, 3174. In someembodiments, the substrate 3180 is an epoxy-based laminate substrate.The substrate 3180 may include other suitable types of substrates inother embodiments. The package assembly 3170 can be connected to otherelectrical devices via a package interconnect 3183. The packageinterconnect 3183 may be coupled to a surface of the substrate 3180 toroute electrical signals to other electrical devices, such as amotherboard, other chipset, or multi-chip module.

In some embodiments, the units of logic 3172, 3174 are electricallycoupled with a bridge 3182 that is configured to route electricalsignals between the logic 3172, 3174. The bridge 3182 may be a denseinterconnect structure that provides a route for electrical signals. Thebridge 3182 may include a bridge substrate composed of glass or asuitable semiconductor material. Electrical routing features can beformed on the bridge substrate to provide a chip-to-chip connectionbetween the logic 3172, 3174.

Although two units of logic 3172, 3174 and a bridge 3182 areillustrated, embodiments described herein may include more or fewerlogic units on one or more dies. The one or more dies may be connectedby zero or more bridges, as the bridge 3182 may be excluded when thelogic is included on a single die. Alternatively, multiple dies or unitsof logic can be connected by one or more bridges. Additionally, multiplelogic units, dies, and bridges can be connected together in otherpossible configurations, including three-dimensional configurations.

Exemplary System on a Chip Integrated Circuit

FIG. 32-33 illustrated exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general-purpose processor cores.

FIG. 32 is a block diagram illustrating an exemplary system on a chipintegrated circuit 3200 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 3200includes one or more application processor(s) 3205 (e.g., CPUs), atleast one graphics processor 3210, and may additionally include an imageprocessor 3215 and/or a video processor 3220, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 3200 includes peripheral or bus logic including a USBcontroller 3225, UART controller 3230, an SPI/SDIO controller 3235, andan I²S/I²C controller 3240. Additionally, the integrated circuit caninclude a display device 3245 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 3250 and a mobileindustry processor interface (MIPI) display interface 3255. Storage maybe provided by a flash memory subsystem 3260 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 3265 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine3270.

FIG. 33A-33B are block diagrams illustrating exemplary graphicsprocessors for use within an SoC, according to embodiments describedherein. FIG. 33A illustrates an exemplary graphics processor 3310 of asystem on a chip integrated circuit that may be fabricated using one ormore IP cores, according to an embodiment. FIG. 33B illustrates anadditional exemplary graphics processor 3340 of a system on a chipintegrated circuit that may be fabricated using one or more IP cores,according to an embodiment. Graphics processor 3310 of FIG. 33A is anexample of a low power graphics processor core. Graphics processor 3340of FIG. 33B is an example of a higher performance graphics processorcore. Each of the graphics processors 3310, 3340 can be variants of thegraphics processor 3210 of FIG. 32.

As shown in FIG. 33A, graphics processor 3310 includes a vertexprocessor 3305 and one or more fragment processor(s) 3315A-3315N (e.g.,3315A, 3315B, 3315C, 3315D, through 3315N-1, and 3315N). Graphicsprocessor 3310 can execute different shader programs via separate logic,such that the vertex processor 3305 is optimized to execute operationsfor vertex shader programs, while the one or more fragment processor(s)3315A-3315N execute fragment (e.g., pixel) shading operations forfragment or pixel shader programs. The vertex processor 3305 performsthe vertex processing stage of the 3D graphics pipeline and generatesprimitives and vertex data. The fragment processor(s) 3315A-3315N usethe primitive and vertex data generated by the vertex processor 3305 toproduce a framebuffer that is displayed on a display device. In oneembodiment, the fragment processor(s) 3315A-3315N are optimized toexecute fragment shader programs as provided for in the OpenGL API,which may be used to perform similar operations as a pixel shaderprogram as provided for in the Direct 3D API.

Graphics processor 3310 additionally includes one or more memorymanagement units (MMUs) 3320A-3320B, cache(s) 3325A-3325B, and circuitinterconnect(s) 3330A-3330B. The one or more MMU(s) 3320A-3320B providefor virtual to physical address mapping for the graphics processor 3310,including for the vertex processor 3305 and/or fragment processor(s)3315A-3315N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 3325A-3325B. In one embodiment the one or more MMU(s)3320A-3320B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 3205, image processor 3215, and/or video processor 3220 ofFIG. 32, such that each processor 3205-3220 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 3330A-3330B enable graphics processor 3310 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

As shown FIG. 33B, graphics processor 3340 includes the one or moreMMU(s) 3320A-3320B, cache(s) 3325A-3325B, and circuit interconnect(s)3330A-3330B of the graphics processor 3310 of FIG. 33A. Graphicsprocessor 3340 includes one or more shader cores 3355A-3355N (e.g.,3355A, 3355B, 3355C, 3355D, 3355E, 3355F, through 3355N-1, and 3355N),which provides for a unified shader core architecture in which a singlecore or type or core can execute all types of programmable shader code,including shader program code to implement vertex shaders, fragmentshaders, and/or compute shaders. The exact number of shader corespresent can vary among embodiments and implementations. Additionally,graphics processor 3340 includes an inter-core task manager 3345, whichacts as a thread dispatcher to dispatch execution threads to one or moreshader cores 3355A-3355N and a tiling unit 3358 to accelerate tilingoperations for tile-based rendering, in which rendering operations for ascene are subdivided in image space, for example to exploit localspatial coherence within a scene or to optimize use of internal caches.

The following clauses and/or examples pertain to specific embodiments orexamples thereof. Specifics in the examples may be used anywhere in oneor more embodiments. The various features of the different embodimentsor examples may be variously combined with some features included andothers excluded to suit a variety of different applications. Examplesmay include subject matter such as a method, means for performing actsof the method, at least one machine-readable medium includinginstructions that, when performed by a machine cause the machine toperform acts of the method, or of an apparatus or system according toembodiments and examples described herein. Various components can be ameans for performing the operations or functions described.

One embodiment provides for a general-purpose graphics processing unitcomprising a set of processing elements to execute one or more threadgroups of a second kernel to be executed by the general-purpose graphicsprocessor, an on-chip memory coupled to the set of processing elements,and a scheduler coupled with the set of processing elements, thescheduler to schedule the thread groups of the kernel to the set ofprocessing elements, wherein the scheduler is to schedule a thread groupof the second kernel to execute subsequent to a thread group of a firstkernel, the thread group of the second kernel configured to access aregion of the on-chip memory that contains data written by the threadgroup of the first kernel in response to a determination that the secondkernel is dependent upon the first kernel.

In one embodiment the scheduler of the general-purpose graphicsprocessor is to configure the second thread group to access the regionof the on-chip memory that contains data written by the first threadgroup in response to the determination that the second kernel isdependent upon the first kernel and that the first thread group and thesecond thread group have a same number of threads. The scheduler canclear at least a portion of the on-chip memory before execution of athird thread group of a third kernel in response to a determination thatthe third kernel is not dependent upon the first kernel or the secondkernel or has a different number of threads than the first thread groupand the second thread group. In a further embodiment, the scheduler canbypass the clear of the region of on-chip memory in response to thedetermination that the second kernel is dependent upon the first kernel.The first kernel can be configured to compute output of a first layer ofa neural network and write output data to the on-chip memory. The secondkernel can read the output data from the on-chip memory and computeoutput of a second layer of a neural network, the first layer of theneural network connected to the second layer of the neural network. Inone embodiment, the on-chip memory includes an implicitly managed cachememory and an explicitly managed shared memory.

One embodiment provides for a method on a parallel processor, the methodcomprising receiving a first kernel and a second kernel for execution ona partition of the parallel processor, detecting that the first kerneland the second kernel have a dependency relationship, scheduling a firstthread group for the first kernel and a second thread group for thesecond kernel for concurrent execution on the parallel processor, andenabling the first thread group and the second thread group to accessoverlapping regions of shared memory. The method can additionallycomprise comprising computing output for a first layer of a neuralnetwork via the first thread group and computing output for a secondlayer of the neural network via the second thread group. The method canadditionally comprise scheduling a third thread group for a third kernelwhen the third kernel does not have a dependency relationship with thefirst kernel and the second kernel and preventing the third thread groupfrom accessing a region of shared memory used by the first thread groupor the second thread group. The method can additionally comprisescheduling a third thread group for a third kernel when the third kernelhas a dependency relationship with the second kernel and does not have adependency relationship with the first kernel, wherein the third threadgroup and the second thread group are enabled to access overlappingregions of the shared memory. In one embodiment the parallel processoris a general-purpose graphics processor. The parallel processor can alsobe, for example, an FPGA.

One embodiment provides for a non-transitory machine-readable mediumstoring instructions to cause one or more processors to performoperations comprising loading shader program code for compilation,detecting that the shader program calls multiple interdependent kernelsusing the same grid size, and marking the interdependent kernels asexecutable without clearing shared local memory between executions ofthe multiple interdependent kernels. In one embodiment the multipleinterdependent kernels are to compute output of multiple successivelayers of a neural network.

One embodiment provides for a circuit board comprising a hostinterconnect, a general-purpose graphics processor coupled to the hostinterconnect, the general-purpose graphics processor including a set ofprocessing elements to execute one or more thread groups of a secondkernel to be executed by the general-purpose graphics processor, anon-chip memory coupled to the set of processing elements, and ascheduler coupled with the set of processing elements, the scheduler toschedule the thread groups of the kernel to the set of processingelements, where the scheduler is to schedule a second thread group ofthe second kernel to execute subsequent to a first thread group of afirst kernel and, in response to a determination that the second kernelis dependent upon the first kernel, the second thread group isconfigured to access a region of the on-chip memory that contains datawritten by the first thread group, and a memory coupled to the hostinterconnect and the general-purpose graphics processor. Thegeneral-purpose graphics processor can also include components of othergraphics processors or parallel processors described herein.

Of course, one or more parts of an embodiment may be implemented usingdifferent combinations of software, firmware, and/or hardware.Throughout this detailed description, for the purposes of explanation,numerous specific details were set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however, toone skilled in the art that the embodiments may be practiced withoutsome of these specific details. In certain instances, well-knownstructures and functions were not described in elaborate detail to avoidobscuring the inventive subject matter of the embodiments. Accordingly,the scope and spirit of the invention should be judged in terms of theclaims that follow.

What is claimed is:
 1. A general-purpose graphics processor comprising:a set of processing elements to execute one or more thread groups of asecond kernel to be executed by the general-purpose graphics processor;an on-chip memory coupled to the set of processing elements; and ascheduler coupled with the set of processing elements, the scheduler toschedule the thread groups of the kernel to the set of processingelements, wherein the scheduler is to schedule a second thread group ofthe second kernel to execute subsequent to a first thread group of afirst kernel and, in response to a determination that the second kernelis dependent upon the first kernel, the second thread group isconfigured to access a region of the on-chip memory that contains datawritten by the first thread group.
 2. The general-purpose graphicsprocessor as in claim 1, wherein the scheduler is to configure thesecond thread group to access the region of the on-chip memory thatcontains data written by the first thread group in response to thedetermination that the second kernel is dependent upon the first kerneland that the first thread group and the second thread group have a samenumber of threads.
 3. The general-purpose graphics processor as in claim2, wherein the scheduler is to clear at least a portion of the on-chipmemory before execution of a third thread group of a third kernel inresponse to a determination that the third kernel is not dependent uponthe first kernel or the second kernel.
 4. The general-purpose graphicsprocessor as in claim 3, wherein the scheduler is to clear at least aportion of the on-chip memory before execution of a third thread groupof a third kernel in response to a determination that the third threadgroup has a different number of threads than the first thread group andthe second thread group.
 5. The general-purpose graphics processor as inclaim 4, wherein the scheduler is to bypass a clear of the region ofon-chip memory that contains data written by the first thread group inresponse to the determination that the second kernel is dependent uponthe first kernel.
 6. The general-purpose graphics processor as in claim5, wherein the first kernel is to compute output of a first layer of aneural network and write output data to the on-chip memory.
 7. Thegeneral-purpose graphics processor as in claim 6, wherein the secondkernel is to read the output data from the on-chip memory and computeoutput of a second layer of a neural network, the first layer of theneural network connected to the second layer of the neural network. 8.The general-purpose graphics processor as in claim 1, wherein theon-chip memory includes an implicitly managed cache memory and anexplicitly managed shared memory.
 9. A method on a parallel processor,the method comprising: receiving a first kernel and a second kernel forexecution on a partition of the parallel processor; detecting that thefirst kernel and the second kernel have a dependency relationship;scheduling a first thread group for the first kernel and a second threadgroup for the second kernel for concurrent execution on the parallelprocessor; and enabling the first thread group and the second threadgroup to access overlapping regions of shared memory.
 10. The method asin claim 9, additionally comprising computing output for a first layerof a neural network via the first thread group and computing output fora second layer of the neural network via the second thread group. 11.The method as in claim 9, additionally comprising scheduling a thirdthread group for a third kernel when the third kernel does not have adependency relationship with the first kernel and the second kernel andpreventing the third thread group from accessing a region of sharedmemory used by the first thread group or the second thread group. 12.The method as in claim 9, additionally comprising scheduling a thirdthread group for a third kernel when the third kernel has a dependencyrelationship with the second kernel and does not have a dependencyrelationship with the first kernel, wherein the third thread group andthe second thread group are enabled to access overlapping regions of theshared memory.
 13. The method as in claim 9, wherein the parallelprocessor is a general-purpose graphics processor.
 14. A non-transitorymachine-readable medium storing instructions to cause one or moreprocessors to perform operations comprising: loading shader program codefor compilation; detecting that the shader program calls a first set ofmultiple interdependent kernels using a same grid size; marking thefirst set of multiple interdependent kernels as executable withoutclearing shared local memory between execution of kernels in the firstset of multiple interdependent kernels; detecting that the shaderprogram calls a second set of multiple interdependent kernels usingdiffering grid sizes; and configuring the shared local memory to becleared between execution of kernels in the second set of multipleinterdependent kernels.
 15. A non-transitory machine-readable medium asin claim 14, wherein the multiple interdependent kernels are to computeoutput of multiple successive layers of a neural network.
 16. A circuitboard comprising: a host interconnect; a general-purpose graphicsprocessor coupled to the host interconnect, the general-purpose graphicsprocessor including a set of processing elements to execute one or morethread groups of a second kernel to be executed by the general-purposegraphics processor, an on-chip memory coupled to the set of processingelements, and a scheduler coupled with the set of processing elements,the scheduler to schedule the thread groups of the kernel to the set ofprocessing elements, wherein the scheduler is to schedule a secondthread group of the second kernel to execute subsequent to a firstthread group of a first kernel and, in response to a determination thatthe second kernel is dependent upon the first kernel, the second threadgroup is configured to access a region of the on-chip memory thatcontains data written by the first thread group; and a memory coupled tothe host interconnect and the general-purpose graphics processor. 17.The circuit board as in claim 16, wherein the scheduler of thegeneral-purpose graphics processor is to configure the second threadgroup to access the region of the on-chip memory that contains datawritten by the first thread group in response to the determination thatthe second kernel is dependent upon the first kernel and that the firstthread group and the second thread group have a same number of threads.18. The circuit board as in claim 17, wherein the scheduler is to clearat least a portion of the on-chip memory before execution of a thirdthread group of a third kernel in response to a determination that thethird kernel is not dependent upon the first kernel or the second kernelor in response to a determination that the third thread group has adifferent number of threads than the first thread group and the secondthread group.
 19. The circuit board as in claim 18, wherein thescheduler is to bypass a clear of the region of on-chip memory thatcontains data written by the first thread group in response to thedetermination that the second kernel is dependent upon the first kernel.20. The circuit board as in claim 19, wherein the first kernel is tocompute output of a first layer of a neural network and write outputdata to the on-chip memory, and wherein the second kernel is to read theoutput data from the on-chip memory and compute output of a second layerof a neural network, the first layer of the neural network connected tothe second layer of the neural network.